WHO MAKES THE NEWS? 6th GLOBAL MEDIA MONITORING PROJECT GMMP Monitoring Day: PNG05 Editor Sarah Macharia Special contributions Monika Djerf-Pierre, Karen Ross, Maria Edstrom and Sandra Lopez Researchers GMMP Global Network in 100+ countries Extra special thanks for persisting during the devastating global Covid-19 pandemic Copy-editors Sara Speicher, Philip Lee and Marites Sison GMMP WACC Secretariat staff and consultants Special thanks to Gisele Langendries, Khodeza Hossain, Lilian Ndangam and Rowan Moses. Research Assistants Drew-Anne Glennie, Gabrielle Sweeny-Tobin and Sohailia Saywack Technical Advisory Committee Amie Joof (Senegal), Azza Kamel (Egypt), Claudia Padovani (Italy), Gitiara Nasreen (Bangladesh), Hilary Nicholson (Jamaica), Jonita Siivonen (Finland), Karen Ross (United Kingdom), Maha Al-Zghary (Palestine), Margaret Sentamu (Uganda), Maximiliano Duenas Guzman (Puerto Rico), Sandra Lopez (Ecuador), Suheir Farraj (Palestine) and Tas-neem Ahmar (Pakistan). Database Code for Africa (CFA). Special thanks to Justin Arenstein, Clemence Kyara, Jean Githae, Isaiah Ngaruiya, Catherine Gicheru, David Lemayian and Samuel Afolaranmi Funding partners UN Women, Free Press Unlimited, WAN-IFRA Women in News, and the Pacific Media Assistance Scheme (PACMAS) ABC afkica •UNišší WOMEN El fr|ee PlFSS We thank The GMMP 1995, 2000, 2005, 2010 and 2015 teams who made possible the longitudinal perspective in this report. Participants at the Women Empowering Communication conference (Bangkok, 1994) whose idea of a global monitoring day gave life to this project. Design Brad Collicottbgraphical.com The designations employed and the presentation of the material in this report do not imply the expression of any opinion whatsoever on the part of the Global Media Monitoring Project concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. ISBN: 978-1-7778038-0-3 Licensed under creative commons using an Attribution-NonCommercial -NoDerivs creative commons 2.5 deed. Noncommercial. You may not use this work for commercial purposes. No derivative works. You may not alter, transform or build upon this work. For any use or distribution, you must make clear to others the license terms of this work. Your fair use and other rights are in no way affected by the above. GMMP GMMP 2020 Who Makes the News? fril "able of Contents Preface ..................................................................... 1 Foreword....................................................................3 Executive Summary..............................................................4 I. The Sample................................................................8 II. News subjects and sources: Progress without revolution......................................20 General patterns.............................................................20 Transnational media...........................................................23 Gender and related: Who makes #MeToo news?............................................25 Women from minority and historically marginalized groups.....................................28 Story scope................................................................32 Functions in the news..........................................................32 Trans and gender minorities in the news................................................33 On news content related to Covid-19..................................................34 Occupations................................................................36 Objectification of women in the news.................................................39 Victims and survivors...........................................................40 III. Reporters and presenters : Nudging the glass ceiling upwards...................................44 General patterns.............................................................44 Story allocation by major topic.....................................................46 Do more women reporters result in greater gender diversity in sources?..............................49 IV. News quality from a gender perspective...............................................53 On gender stereotypes..........................................................53 Rights-centred journalistic practice...................................................55 Women's centrality in the news .................................................56 Gender (in)equality in the news ..................................................59 Does the reporter's gender matter for gender integration in stories?................................63 Action Plan 2021-2025 ........................................................... 65 Annex 1. References............................................................67 Annex 2. Methodology expanded discussion...............................................69 How the monitoring took place.....................................................69 How media bands were created.....................................................70 How media weights were created....................................................70 How accuracy was guaranteed......................................................70 Limitations................................................................71 About Code for Africa...........................................................71 Credits...................................................................71 Annex 3. List of topics...........................................................72 Politics and Government.........................................................72 Economy..................................................................72 Science and Health............................................................72 Social and Legal.............................................................73 Crime and Violence............................................................73 Gender and related............................................................73 Celebrity, Arts and Media, Sports....................................................73 Other....................................................................73 Annex 4. Participating teams and data sample..............................................74 GMMP2020 iii VVI Annex 5. Data tables............................................................77 1. Gender equality in news media content index (GEM-I). 2020.................................... 78 2. Sex of presenters, reporters and news subjects & sources in newspaper, television and radio news..............79 3. Subjects & sources in newspaper, television and radio news...................................82 4. Subjects & sources in newspaper, television and radio news, by major topic areas.......................85 5. Subjects & sources in newspaper, television and radio news, by major occupational groups..................88 6. Function of subjects & sources in newspaper, television and radio news.............................91 7. Subjects & sources in newspaper, television and radio news described as victims........................94 8. Subjects & sources in newspaper, television and radio news, mentioned by family status...................96 9. Subjects & sources quoted directly in newspapers.........................................99 10. Subjects & sources appearing in newspaper photographs...................................101 11. Presenters and reporters in newspaper, television and radio news...............................104 12. Reporters in print, television and radio news, by major topic areas...............................107 13. Subject and source selection by sex, by sex of reporter in print, television and radio stories................110 14. This story clearly challenges gender stereotypes. Responses on print, television and radio news..............112 15. This story clearly highlights issues of gender equality or inequality. Responses on print, television and radio news . . . 114 16. This story quotes or makes reference to legislation or policy that promotes gender equality or human rights. Responses on print, radio and television news..................................................116 17. News websites and news media tweets. Sex of reporters and news subjects & sources...................118 18. News websites and news media tweets. News subjects & sources, by sex...........................121 19. News websites and news media tweets. News subjects & sources in major topic areas, by sex...............124 20. News websites. Subjects & sources in major occupational groups, by sex...........................127 21. News websites - Function of subjects & sources, by sex.....................................130 22. News websites. Subjects & sources described as victims, by sex................................133 23. News websites. Subjects and sources who are quoted directly, by sex.............................136 24. News websites and news media tweets. Subjects & sources appearing in images and video plug-ins, by sex.......139 25. News websites and news media tweets.Reporters in major topic areas, by sex........................142 26. News websites and news media tweets. Responses to "This story deary challenges gender stereotypes".........145 Annex 6. List of coordinators.......................................................147 Annex 7. Technical advisory committee.................................................152 Annex 8. Resources for Journalists...................................................153 List of tables Table 1. Participating teams 1995 - 2020 .............................9 Table 2. Stories monitored. 2020 ................................ 10 Table 3. Breakdown of news items by region. 2020 .......................... 10 Table 4. Is this story related to Covid-19? By major topic, by medium....................11 Table 5. Is this story related to Covid-19? Television, by region......................11 Table 6. Topics in the news. 2005-2020.............................. 13 Table 7. Top 10 news topics on the global monitoring day 29 September 2020 ................. 15 Table 8. Topics in newspaper, television and radio news. Regional comparisons. 2020............... 15 Table 9. Topics on News websites and News media Twitter feeds news. Regional comparisons. 2020 .......... 16 Table 10. Key Findings: 1995 - 2020............................... 17 Table 11. Major topics by space in newspapers. 2020 ......................... 19 Table 12. Women subjects and sources by medium. 1995-2020 ...................... 21 Table 13. Overall presence of women in print, radio and television news, by region. 1995-2020 ........... 21 Table 14. Overall presence of women in print, radio and television news, by major topic, by GMMP year. 1995-2020 ..... 22 Table 15. Women subjects and sources in print, radio and television news, by major topic, by region. 2020 ........ 22 Table 16. Overall presence of women in transnational news sites. 2020 ................... 23 Table 17. Women subjects and sources in content on news websites and news media tweets, by major topic, by region. 2020 . . 24 Table 18. Gender and related news sample, percent distribution within major topic by media type. 2020 ........ 26 Table 19. Reporting on gender-based violence, subjects and sources, % women, by region. 2020 ........... 27 Table 20. News subjects and sources from minority and historically marginalized groups. 2020 ........... 28 Table 21. Top 10 topics* in which women are most likely to be present in print, television and radio news. 2020 ...... 29 Table 22. Women's presence in news topics in print, television and radio news ...the bottom 10. 2020 ......... 30 Table 23. Women as news subjects in different story topics in print, television and radio news. 2020........... 30 Table 24. Female news subjects in local, national, regional and international stories in newspapers, television and radio. 1995-2020.............................. 32 Table 25. News subjects and sources. % Women, by function, by medium. 2005-2020................ 33 Table 26. Comparing Covid-19-related and non-Covid stories. 2020..................... 34 GMMP 2020 iv Who Makes the News? Table 27. Subjects and sources in Covid-19 news. % Women, by major topic, by medium. 2020............. 35 Table 28. Comparing Covid-19-related and non-Covid stories on Television, Functions of subjects and sources, %Women. 2020. . 35 Table 29. Subjects and sources in Covid-19 news. % Women, by function, by medium. 2020.............. 35 Table 30. Functions of female news subjects, by region. 2020....................... 36 Table 31. Women's share of occupations according to the news. 2000-2020................... 36 Table 32. Top 5 occupations for women and men according to the news. 2020 ................. 38 Table 33. Functions of news subjects, by sex, by occupation. 2020...................... 38 Table 34. Age of news subjects in newspapers, % Women. 2005-2020..................... 40 Table 35. Victims and survivors in the print, television and radio news, by sex. 2005-2020 ............. 41 Table 36. Reporters and presenters. 1995 - 2020 .......................... 45 Table 37. Female presenters and reporters in print, radio and television news, by region. 2000-2020 .......... 45 Table 38. Female reporters in print, radio and television news, by region. 2000-2020................ 46 Table 39. Stories by female reporters in traditional mediums, by scope. 1995-2020................ 46 Table 40. Stories by female reporters in traditional mediums, by major topics. 2000-2020 ............. 47 Table 41. Female reporters in print, television and radio stories, by major topic, by region. 2020............ 49 Table 42. Female news subjects, by sex of reporter. Print, television and radio stories, 2000-2020............ 49 Table 43. Female news subjects, by sex of reporter. News websites. 2015-2020.................. 50 Table 44. Top 10 news stories most likely to be reported by women. 2020 .................. 50 Table 45. Stories least likely to be reported by women...the bottom 10*. 2020 ................. 50 Table 46. Topics in the news - Detail by medium for female reporter. 2020 .................. 51 Table 47. Stories that clearly challenge gender stereotypes, by major topic. 2005-2020............... 54 Table 48. Stories that clearly challenge gender stereotypes, by region, by major topic. 2020............. 54 Table 49. Stories that clearly challenge gender stereotypes, by region. 2005-2020................. 54 Table 50. Reference to gender equality/human rights/policy, by major topic. 2015-2020............... 55 Table 51. Reference to gender equality, women's rights and/or human rights policy, by region. 2010-2020......... 55 Table 52. Reference to gender equality/human rights/policy, by major topic by region. 2020............. 56 Table 53. Women's centrality in the news, by major topic. 2000-2020..................... 56 Table 54. Top 10* topics in which women are most likely to be central. 2020 ................. 57 Table 55. Women's centrality...the bottom 10 stories. 2020 ....................... 57 Table 56. Stories with women as a central focus, percentage by topic -detail. 2020................ 58 Table 57. Stories where issues of gender equality or inequality are raised, by region. 2005-2020............ 59 Table 58. Stories where gender equality issues are raised, by major topic, by region. 2020.............. 60 Table 59. Stories where issues of gender equality/inequality are raised by major topic. 2005-2020........... 60 Table 60. Top 10 news stories in which gender equality issues are most likely to be raised. 2020 ........... 61 Table 61. Raising gender (in)equality issues... the bottom 10 stories. 2020 .................. 61 Table 62. Stories where issues of gender equality/inequality are raised by topic-detail. 2020 ............ 62 Table 63. Gender difference in reporting: On clearly challenging gender stereotypes. 2010-2020............ 63 Table 64. Gender difference in reporting: On gender (in)equality. 2005-2020.................. 63 Table 65. Gender difference in reporting, by region: On gender (in)equality. 2015-2020 .............. 64 Table 66. Gender difference in reporting, by major topic: On rights-based journalism. 2015-2020............ 64 List of Figures Figure 1. Covid-19 density between January 1 and September 30, 2020 ................... 12 Figure 2. Indigenous women as a proportion of indigenous peoples in Latin American news............29 Chart 1. GMMP 2020: Comparing health specialists in Covid-19-related news, % women, and doctors in the physical world, % women..........................37 Figure 3. Age of subjects and sources in print news. Distribution by sex. 2020.................. 39 Chart 2. Correlating Gender Equality in the News and Level of Democracy..................43 Figure 4. Reporters by major topic, by sex. Comparing newspapers and news websites. 2020 ............ 47 Figure 5. Female news subjects by sex of reporter. Print, television and radio news. 2020 ............. 49 GMMP 2020 v Who Makes the News? In Homo Deus, Yuval Noah Harari writes about two selves that co-exist in every person: the experiencing-self and the narrating-self.l He describes the experiencing-self as a moment-to-moment consciousness, that "remembers nothing, and tells no stories and is seldom consulted when it comes to major decisions". In contrast, there is the narrating-self, which retrieves memories, tells stories, and makes big decisions. Crucially, the narrating-self "doesn't aggregate experiences - it averages them." In short, the narrating-self clings to the familiar and comfortable, seeking points of conformity and least resistance, in order to protect itself in a world of contradiction and confusion. This immediately raises the question of where an individual obtains the information and knowledge that allows the narrating-self to position itself and to create a worldview. The answer is by no means straightforward. Yet, at any given time, it must be based on an accretion of memories, stories, and data that the individual has encountered socially and culturally. In other words, from childhood (and perhaps before birth) patinas have built up that filter perceptions and understandings, and directly or indirectly influence behaviours and actions, attitudes which unexamined can last a lifetime. Children and young people are likely to be most susceptible to this accumulation of layered meanings, which can often only be altered by broader experience: a change of perspective (crossing a bridge to see the view from the other side), literature that invites self-examination, films that explore life's greatest questions, and by balanced and unbiased information and news. What we see, hear, and read in media of all kinds affects individual and collective thinking and action - filtered in turn by the narrating-self. People's perceptions about life and death, peace and conflict, justice and injustice, and women and men, are coloured - sometimes imperceptibly, sometimes boldly - by what seem to be majority views in a form of socio-cultural conditioning. When millions of people on social media endorse a product or believe obviously fake news, it is difficult to persuade them otherwise. The media have acquired a power to shape political, social, and cultural norms and beliefs out of all proportion to their function as bearers of information. It is a power that the communication rights movement - of which WACC and its Global Media Monitoring Project (GMMP) are part -intends to hold to account. While media depict the realities of society, when it comes to gender relations, they also help construct it by reinforcing misperceptions, imbalances, and perceived differences between women and men. The GMMP is needed precisely because it invites the world's news media to redress such blatant discrimination. Who is seen and heard in the news? Who writes and produces the news and from what perspectives? How do newsrooms operate? What policies do media outlets follow? How are young journalists taught their craft regarding media ethics and accountability? Since 1995 and at five yearly intervals, the GMMP has shown that news paints a picture of a world in which women, in proportion to men, are dramatically under-represented and made invisible. A comparison of the results between 1995 and 2015, revealed that change in the gender dimensions of news media was small and slow. Only 24% of news subjects - the people interviewed or whom the news is about - were female. Women's points of view were less frequently heard in the topics that dominated the news agenda; even in stories that affected women profoundly, such as gender-based violence, the male voice prevailed. When women did make the news, it was primarily as "stars" or "ordinary people", not as experts, professionals, or figures of authority. While the studies turned up some exemplary gender-balanced and gender-sensitive journalism, overall they demonstrated a glaring deficit in the news media globally: half the world's population was barely present. At the same time, we must remember that the news media are only one part of the contemporary information habitat: GMMP 2020 1 Who Makes the News? those places people see and hear themselves and others. Films, documentaries, novels, reality TV, soap operas, magazines, advertisements, and above all social media platforms jostle for attention in a world bent on portraying and informing itself. How is gender represented in these media and how do they influence each other? Intersection-ality has come to be known as a framework for understanding how aspects of people's political, social, and cultural identities combine to create modes of discrimination and privilege. We may now need to examine intersectionality in the media to reveal their interconnectedness and to consolidate demands for change. However, one difficulty is that only a meagre proportion of the human and financial resources invested in advancing gender equality goes towards work on gender and media. This area of work has struggled to become a priority (the UN Sustainable Development Goals give it marginal room) and it is currently sliding further into the background. In addition, there is the intractable problem of embedded social and cultural norms that feed into and are fed by media content. It is, of course, extremely difficult to prove the connections, although many among those struggling for gender equality talk about it. And, inevitably, there is the fundamental problem of patriarchy embedded in all institutions, including the media. The findings of GMMP 2020 reinforce the perception that that there is still a long road ahead to "achieve gender equality and empower all women and girls" (Sustainable Development Goal 5). Identifying strengths, weaknesses, successes, and failures in the ways women and girls appear in the world's news media is part of a larger, collective endeavour to transform information and communication systems. Only then will Harari's "narrating-self" find fairness, balance, and equality in news media content. When that happens, it will be due in no small measure to the long-term dedication of a global team of coordinators inspired and led by Dr Sarah Macharia, and to the determination of a very large team of volunteers worldwide, for whom failure is not only unpalatable, but unthinkable. As the GMMP demonstrates, studying how women and men are represented in the news is important because often what people see is what they believe. And when it comes to gender, rectifying the mistaken assumptions caused by discrimination, misogyny, and patriarchal beliefs can only be done through a clearsighted reappraisal and revision of news policies and practices. Philip Lee WACC General Secretary Note l.Yuval Noah Harari (2016). Homo Deus: A Brief History of Tomorrow. Signal Books. GMMP Monitoring Day: (previous page, Left to right) Jordan, Nigeria, Serbia, Mali; (below) PNG07_GMMP2020.jpg Foreword As countries look to rebuild economies that are greener, more resilient and gender-equal in the wake of the COVID-19 pandemic, we need- more than ever- stories that reflect the diversity of women's expertise and perspectives in the global news media. Yet this report reveals that women, especially the most marginalized among them, remain shockingly underrepresented in the media and in global news coverage. For the past year, the majority of the global news coverage has been dominated by COVID-19, yet the data shows us that women's voices have been yet again largely absent from the conversation. When women are on average 46 per cent of health specialists in reality, but appeared as such in just 27 per cent of coronavirus stories, inaccurate gender stereotypes are reinforced. At a time when a 'shadow pandemic' of violence against women and girls raged around the world, the fact that only 6 out of 100 stories were related to sexual harassment, rape and sexual assault against women risks normalizing gender-based violence. UN Women is proud to support the Global Media Monitoring Project (GMMP) report, and its strong, evidence-based wake-up call to create change in the media industry. Increasing the representation of women and other gender minorities in news coverage is vital; not only as subjects but as experts and professionals, as well as increasing women's leadership in newsrooms and boardrooms. The media can also play a crucial role by refusing to perpetuate stereotypes, such as those that portray women solely as victims or homemakers. We have seen how fast traditional gender stereotypes are reasserted when crisis strikes, especially at home where decisions about caregiving work are made. The high-quality data and analysis provided in this report are essential to understanding the problem and making the case for urgent action. By hearing more women's voices in the news as experts and leaders, and by seeing their stories featured centrally in ways that push against simplistic stereotypical gender roles, the media can create the more accurate, inclusive and empowering representation we need as the world rebuilds. UN Under-Secretary-General and Executive Director of UN Women, Phumzile Mlambo-Ngcuka GMMP 2020 «ja*»- ■ .......... ............. ........_ 1 onom Na Uitiveniff tu Kvm Me- »«*eS„i>- na kojcm Akstntijtviícva v* c1ľi "' H, trniutno sp odvijaju kli- tó « >í SS%3%^J&uiťia fepitwanja vaktine proliv - ^ '' " Parana. SPS* -Jmpreslviiojekakoíe NADÁ DA rf nauíiu zaicdjiiĽi za vrio krat- 01 EC miuln vise O íCKfucIrri tentptiana", ja imala i íastmVu juíi uticaj na nauku Medutim, isdre ůa nijf sav nápor nauke slmnten-trísan samo na pronalaženje vakáae. "Za vrijeme karantina iiuľľíiilí ímaju više vroucná ľteqcj šnaŕe za onlajii susreta s kolňjama aran svtjäa - uz pomoc msenielaiigostila sam nr-kí' od sujwrih vtmrrijaka baí u svojej íJnevnoJ soli", iiavudi KaD Sto nijp laiistavfla su-srrte i aradnju nauórika, cpi Executive Summary The emergence and rapid proliferation of Covid-19 made the 2020 implementation of the Global Media Monitoring Project (GMMP) the most extraordinary since the initiative's inception in 1995. Yet, despite the pandemic, the number of participating countries, media and stories monitored was the highest ever. GMMP 2020 was implemented in 116 countries and covered 30,172 stories published in newspapers, broadcast on radio and television, and disseminated on news websites and via news media tweets. Twenty-five per cent of stories in the sample carried a coronavirus sub- or principal theme. A tweak in the methodology still made it possible to analyze the stories along the classic GMMP major topic categories of politics & government, economy, science & health, social & legal, crime & violence and celebrity/media/arts & sports. The GMMP 2020 topics' structure carved out a seventh major topic "gender & related", in which to cluster stories specific to sexual harassment, rape, #MeToo and similar gender-specific stories. All things remaining equal, it will take at least a further 67 years to close the average gender equality gap in traditional news media. In 2015, the period remaining to full gender equality based on the GEM Index was 72 years, thus the 2020 result signals consistency in the slow cumulative pace of change over time. Full gender equality on numerical counts, however, is insufficient without improvement in the quality of journalism from a gender perspective. At the global average level, mainstream news media are currently at the midway point to gender parity in subjects and sources. Between 2015 and 2020, the needle edged one point forward to 25% in the proportion of subjects and sources who are women. The single point improvement is the first since 2010 and is most visible in broadcast news media. Despite their three-point decline in the proportion of women subjects and sources since 2015, North American news media remain the best performers worldwide. European news media have made the most significant progress on this indicator since 1995 and Pacific region media in the past five years. Only Africa's media have stagnated as the rest of the regions have improved by three to 12 points across the quarter century. The proportion of women as subjects and sources in digital news stories also increased one point overall from 2015 to 2020, with a three-point improvement on news websites and a three-point decline in news media tweets. The overwhelming majority of science/health news was related to Covid-19, the limelight story of 2020. The meteoric climb in this major topic's news value due to the pandemic has been accompanied by a fall in women's voice and visibility in the stories. While the news share of science/health stories was significantly higher in 2020 compared to earlier periods (from 10% in 2005 to 17% currently), women's presence in this topic declined by five points after a steady rise between 2000 and 2015. Women's overall presence in the news in North America and the Pacific has surpassed the critical 30% threshold in GMMP 2020 4 Who Makes the News? (r) both digital and legacy media. Africa falls below the global averages across all media types monitored, as do Asia and the Middle East in print and broadcast news. The only region and topic in which gender parity in subjects and sources has been attained is in North American digital social & legal news. The Gender Equality in the News Media index (GEM Index or GEM-I) calculates the average gender equality gap based on six GMMP indicators: in people in the news (subjects & sources), in participation as reporters, in voice as experts and as spokespersons, and in presence in economic and in political news. Details on the calculation and individual country scores are indicated in Annex 7 table 1. Transnational media perform poorly with regard to inclusion of women as subjects and sources. Women were only 13% of subjects and sources in the television newscast monitored and 21% in the digital news stories and tweets coded from Al Jazeera, BBC News -World, CNN International, France 24, Reuters, RT News, TeleSur and @nytimes. In 2015, women were 15% of the people seen, heard, or read about in transnational digital outlets. While the results have improved, women's invisibility remains even more marked in influential international media that serve formidable audiences. #MeToo: The pattern of underrepresentation of women even in stories that concern them more spills over in news content on gender-based violence Stories on gender-based violence (GBV) hardly make the major news of the day and when they do, women and girls are severely underrepresented as subjects and sources. Just 1% of the stories in the total sample were coded under the "gender and related" major topic that includes news on various forms of gender violence against women and girls. Furthermore, that girls and women are underrepresented in stories about sexual harassment, rape and sexual assault particularly now, during Covid-19 times when such acts have reached epidemic proportions, signals a serious deficit in news media accountability to women. The most severe underrepresentation in GBV stories takes place in newspapers, in which women are just 35% of subjects and sources. Comparison of the GMMP findings against physical world statistics indicates that women are underrepresented across all the identity groups. In Latin America for example, only 3% of the people in the news are from Indigenous or tribal groups and of these only one in five is a woman. In the physical world, however, Indigenous peoples are estimated to be at least 8% of the region's population, and women at least one half of the Indigenous population. The results demonstrate women's multiple marginalization based on their subordinate identities in the respective contexts. The failure to extend the opportunity for more citizens to tell their own stories in their own words, to tell the stories which are important to them and, also, to a broad range of people, compromises the value of the news to its multiple and diverse publics. The failure to represent the diversity of people and opinion present in society not only has implications for public discourse and decision-making, but it also plays a role in eroding trust in news journalism. Appreciable gains in women's presence as authoritative sources Women's voice as spokespersons has risen by eight points since 2005, and as experts by seven points in the same period. In recent years numerous initiatives to source women for expert opinion have sprouted around the globe and media organisations are visibly making efforts to diversify their experts' pools, responding to external pressure as well as internal industry efforts to do better. Currently, 24% of expert voices in the news are women, a dramatic rise from 19% five years ago. In keeping with the historical patterns, women are still more likely to appear in unexceptional roles as personal experience providers (42% in traditional media, 41 % in news websites) and popular opinion givers (38% in traditional media, 39% in news websites). Gender-lens-deficient pandemic news coverage Overall, women's presence as subjects, sources and journalists in stories related to Covid-19 maybe higher than in stories that are not about the pandemic, but the quality of content from a gender perspective is worse. Stories about or regarding a dimension of the coronavirus focus on women four points less than stories not linked to Covid-19, and they are less likely to raise gender equality or inequality issues, or to clearly challenge gender stereotypes. Women are more likely to appear in pandemic stories related to social/legal issues, while the possibility that a story will be about a woman or will carry a woman's voice is slimmest in Covid-19 stories that are also about politics and government. Multiple jeopardy in visibility and voice for minority and historically marginalized women Teams in 81% of the participating countries took the opportunity provided by GMMP 2020 to collect data on indicators of interest in the national context. A number of these indicators made it possible to unpack the results using intersectional lenses, to understand news media treatment of subjects and sources on the basis of their other identities such as race, religion, class/caste, immigration and disability status. Gender equality in the world depicted in the news still lags behind gender equality in the physical world. While understanding and acknowledgement of women's contributions have grown in the lived world, the same would not be said of the news media. An example is provided in pandemic stories: women are 27% of the health specialists appearing in coronavirus stories, far fewer than the 46% world average given in labour force statistics. Of the persons portrayed as homemakers, women are almost seven in 10, similar to the 2015 findings. Similarly, their GMMP 2020 5 Who Makes the News? ranks among the unemployed as portrayed in news reports, have increased by about eight points in the past five to 20 years. In reality, World Bank modelling of the ILO's sex-disaggregated labour force statistics suggests that unemployment rates have reduced for men by 0.4 points and even more for women by 0.5 points since the year 2000. Gendered ageism in the news 2020 is the first time that the GMMP investigated the representation of people 80 years and above in the news. 2020 was also the first year of the global Covid-19 pandemic, where old age was considered a common denominator for being at risk. However, people in the oldest age group rarely got attention in the news: only 3% were above 80 years in newspapers, and in television news less than 1% were above 80 years of age. Women 80+ were even more invisible than the men in that age group. Overall in print news, men who are 50 years and older are very likely to be in the news; 42% of all people in the news belong to this age group. The largest age category for women is 35-49 years, whereas men peak in visibility at 50 to 64 years. Over time in newspapers and on television, women above 50 have become more invisible. Only 3% of all women in the news are between 65-79, compared to 15% of the men. Following stagnation between 2005 and 2015, women's visibility as reporters has increased by three percentage points overall across print and broadcast news. Currently, four out of 10 stories in traditional news media are reported by women, compared to 37% since 2005. In the past two decades, women's newspaper byline credits have increased by 11 points, their visibility in newscasts has increased by 9%, and online, 42% of journalists named in news articles, seen or heard in multimedia clips are women. A comparison between print and digital newspapers reveals that stories by women reporters are distributed more or less evenly across the major topics online and offline, as those by men are skewed towards the politics & government beat. The reporter gender gap is exactly the same in Asia, Europe, and Latin America despite variations in the pace of change on this indicator across two decades. Pacific media have progressed slower than the rest of the world, but they are currently the second-best performers after their Caribbean counterparts. {<■:.:• "j ■■■! -:i GMMP Monitoring Day: Myanmar GMMP 2020 Who Makes the News? The sex of the reporter matters for the gender dimensions of the story GMMP findings across time indicate that women reporters are more likely than men to turn to women subjects and sources. In 2015, the results suggested that the gender source selection gap was narrowing, but in the 2020 wave, the gap has more than doubled to reach 7 points. Currently, 31% of the people in traditional news covered by women reporters are female, in contrast to 24% of subjects and sources in stories by men reporters. There is a consistent 5-7% point gap between women and men reporters on female source selection in all regions except for the Caribbean, where men reporters are almost as likely as their women colleagues to select female sources. The pattern is repeated on digital news platforms where there is a nine-point gap in gender source selection, with 34% of female sources in stories by women reporters compared to 25% in stories by men reporters. Story quality from a gender perspective tends to be marginally higher in the output of women journalists, in terms of likelihood to clearly challenge gender stereotypes, to raise gender (in)equality issues and to make reference to legislation or policy that promotes gender equality or human rights. Even with the gender difference, it is important not to lose sight of the overall decline or stagnation across time on these indicators in the output of all journalists, women and men alike. Patterns of stagnation and decline are consistent across the GMMP measures of the quality of news journalism from a gender perspective. News stories are as (un)likely to clearly challenge gender stereotypes today as they were 15 years ago. Between seven to nine out of 10 stories on sexual harassment, rape, other forms of gender violence and specific gender inequality issues reinforce or do nothing to challenge gender stereotypes, with implications for the normalization and continuance of the very injustices that are the focus of the stories. Fewer than half of gender-related (sexual harassment, rape, other forms of GBV...) stories actually highlight gender (in)equality issues. If Radio Ciudad del Mar a radio... I5h Hi Omara Portuondo y la Orquesta Aragon, entre los nominados a los ^Grammy Lathes 2020 rcm.o.i/qrammylatinos-.., u Kenya u dttiiŕ cut m p+ujec at lal tut JfeMortttnent ot la unlé des femme* UVJUTIil rS?*WO^"í I VflMV«-ľTflff\ Kín rĽrtip-vi í y inrv1 v ríW .i^í^jík ^'rr-to j W i -v ;«J2 rmfjm -tíha ji-y: •w dvomi vivaifO n-mn c-u io ® Las Angeles Times C' '-Ag/i ^.-Pfi j9 Barren tied SC 'eí-skus group (hat ex-members s*y subordinates worner* Sarren ti M 10 retagious group í ha f ex- ; 'ú' tí"* : :n> Q 34 U u Internet News and Tweets on GMMP Monitoring Day (top to bottom): Cuba, France, IsreaL, USA. GMMP 2020 7 In this context, we examined various strategies aimed at strengthening and empowering our communications. They include: [to] organise one day at the start of 1995 for the monitoring of all media and use data as the basis for an analysis of where women are. Excerpt from the Bangkok Declaration, 1994. (1) Online media contents reproduce the exclusion and ghettoization of women, both within the media product and in the comments and responses of new interactive audiences that become co-authors of the process of promoting and legitimizing misogyny as public discourse; informational-communicational technologies themselves do not alter inequalities, but are positioned within social relations mapped by unequal and unjust economic, cultural and political power relationships of neoliberal, patriarchal and heteronormative domination. Excerpt from the New York Declaration, 2017. (1) i The Sample By definition and design, the GMMP captures a snapshot of gender on one "ordinary" news day in the world news media. An ordinary news day is one in which the news agenda contains the run-of-the-mill mix of stories, everyday articles on politics, economy, social issues, crime, etc. By the fifth GMMP in 2015, we had concluded that "ordinary" news days cannot be predicted or planned in advance: unexpected events take place that dominate the news, from the Kobe earthquake in 1995, to the Germanwings plane crash in the Alps in 2015. Events during the sixth GMMP in 2020 were even more extraordinary; beginning in late 2019 and intensifying during the year, the world was ravaged by the novel coronavirus Covid-19. According to Al Jazeera's the Listening Post programme "2020 hindsight: The coverage of Covid-19", the first reports on the virus were carried in media outside China. In early January, the Hong Kong SAR PRC media was already comparing the virus to the SARS outbreak. Acting on information outside People's Republic of China, Al Jazeera reporters went into China in search of the stories. Chinese state media began airing the stories after the death of Dr. Li Wenliang, the doctor later dubbed a "whistleblower" for raising awareness about the mysterious illness. Towards the end of March, Al Jazeera featured news calling for reliable information amid mixed messages by other news networks oftentimes marred by misinformation. By this time journalists had begun reporting "lockdown style", from their homes, similar to numerous other professionals working remotely for the most part of 2020 as part of the Covid-19 containment measures. By the end of March, Covid-19 was no longer a lone article in the news, it was THE news across local, national and international news media worldwide. Conservative political leaders fuelled attacks on reputable news media outlets, alleging Covid-19 to be fake or hyped news. At the same time, news audiences grew exponentially in 2020, shattering historical records as the public craved information on the pandemic. News ratings went up by 50% in India, 64% of UK viewers were watching more live TV than before the lockdown while evening newscasts in the US reached their highest rating in 20 years. The sixth GMMP thus offered an opportunity to scrutinize gender in media coverage during a global catastrophe, a time marked by an unprecedented health crisis, and the intensified gender and socio-economic inequalities accompanying the crisis. GMMP teams in 116 countries (Table 1) monitored 30,172 stories published in newspapers, broadcast on radio and television, and disseminated on news websites and via news media tweets (Table 2) in 2251 news outlets. In spite of the pandemic, the final number of participating countries, media and stories monitored is the highest since the first edition in 1995. The number of participating nations increased by 63% since 1995 as baseline data were collected for eight countries1 joining the study for the first time. More than one half of countries in each world region with the exception of the Pacific, the Middle East and Asia are being represented in the current and previous waves. The number of news items monitored has doubled over the past 25 years and risen by over 8,000 since the 2015 edition. The stories in 2020 were more or less evenly distributed across the traditional mediums overall and in most regions while from the digital sample, almost six out of 10 stories were from news websites. (Table 3) 1 Central Africa Republic, The Gambia, Myanmar, Cayman Islands, Dominica, Greenland, Moldova and Iraq GMMP 2020 8 Who Makes the News? Table 1. Participating teams 1995 - 2020 1995 2000 2005 2010 2015 2020 Africa 12 11 18 27 32 30 Asia 14 14 11 13 11 16 Caribbean 4 6 6 11 15 12 Europe 21 21 24 32 30 29 Latin America 10 8 11 13 14 15 Middle East 3 4 2 6 6 8 North America 2 2 2 2 2 2 Pacific 5 4 2 5 4 4 TOTAL 71 70 76 109 114 116 GMMP2020 9 Who Makes the News? ([]) Table 2. Stories monitored. 2020 Print Radio Television News websites News media Twitter feeds TOTAL Africa 1354 782 794 414 343 3687 Asia 1442 519 1248 1209 533 4951 Caribbean 248 311 261 290 223 1333 Europe 2387 2094 2284 2279 1654 10698 Latin America 889 1371 1603 875 1163 5899 Middle East 403 318 405 565 120 1811 North America 230 128 145 104 79 686 Pacific Islands 246 134 253 185 163 979 Transnational 9 87 32 128 TOTAL 7199 5657 7002 6004 4310 30172 People's Republic of China Global Television Network (CGTN) Africa, Aljazeera, BBC News -World, CNN World, France 24, Reuters, RT News.TeleSur, ©nytimes Table 3. Breakdown of news items by region. 2020 TRADITIONAL DIGITAL Print Radio Television News websites News Media Twitter Africa 46% 27% 27% 55% 45% Asia 45% 16% 39% 69% 31% Caribbean 30% 58% 32% 57% 43% Europe 55% 31% 34% 58% 42% Latin America 23% 35% 41% 45% 57% Middle East 56% 28% 36% 82% 18% North America 46% 25% 29% 57% 43% Pacific Islands 39% 21% 40% 55% 47% Transnational 0% 0% 100% 88% 13% OVERALL 36% 28% 35% 58% 42% TELEVISION PRINT NEWS MEDIA TWITTER FEEDS NEWS WEBSITES GMMP 2020 10 Who Makes the News? Towards the end of the first trimester of the year, the news media agenda was overwhelmed by stories about the pandemic yet by the global monitoring day September 29 the stories had diversified to include the spread of topics observed in pre-Covid-19 years. The GMMP day was scheduled initially for the first quarter of 2020. However, it quickly became clear that proceeding as planned would result in a news sample that would almost entirely be focused on coronavirus stories. The methodology chapter later in this report discusses the stops put in place to ensure a more even - instead of a Covid-heavy - news sample, and overall to mitigate new risks to the project due to the virus. 25% of stories in the total sample carried a coronavirus sub- or principal theme, ranging from 22% of tweets to 27% of radio items. (Table 4) The proportion of stories related to Covid-19 varied across regions. On television, the number ranged from four out of 10 stories in North America to only slightly over 1 in 10 in Africa. Between 20 to 30% of televised stories in all other regions covered the pandemic apart from the Middle East with 35% of the telecasts and North America with 40%. The cross-regional variation of Covid-19 stories appears to follow the pattern of infections across the globe (Figure 1) but a statistical test would be needed to determine whether a correlation actually exists. Table 4. Is this story related to Covid-19? By major topic, by medium Print Radio Television News websites News media Twitter feeds Politics and Government 13% 15% 11% 15% 16% Economy 30% 31% 31% 32% 26% Science and Health 62% 71% 67% 66% 60% Social and Legal 20% 18% 19% 19% 16% Crime and Violence 5% 3% 6% 5% 4% Gender & Related 10% 10% 7% 9% 2% Celebrity, Arts and Media, Sports 18% 20% 14% 17% 10% Other 10% 7% 11% 17% 10% OVERALL 24% 27% 25% 26% 22% 22-27% Of Stories in 2020 were COVID RELATED Table 5. Is this story related to Covid-19? Television, by region Yes No Yes No Africa 12% 88% Latin America 27% 73% Asia 22% 78% Middle East 35% 65% Caribbean 24% 76% North America 40% 60% Europe 28% 72% Pacific Islands 25% 75% OVERALL 25% 75% GMMP 2020 11 Who Makes the News? Figure 1. Covid-19 density between January 1 and September 30, 2020 Data sources WHO Coronavirus Disease Dashboard https://covidl9.who.int/. Total number of new cases between January 1 and September 30,2020 World Development Indicators, the World Bank. Population Total, 2019 Created with Datawrapper A comparison of stories by major theme reveals a distribution more or less similar to previous years apart from the expected jump in the proportion of stories about science & health. The decision to adjust the coding instruments to capture secondary topics for stories that carried a Covid-19 angle as well worked to ensure that results could be compared with earlier GMMPs. (Table 6) This tweak in the methodology made it possible to analyze the stories along the classic GMMP major topic categories (see annex 3) while avoiding a skew in distribution caused by a pandemic-heavy news agenda. The adjustment as well enabled a re-casting of the stories under two binary categories - Covid-related and non-Covid stories - for a closer assessment. Similar to previous GMMPs (except for 20152), political stories dominate the news: Currently, close to one in four stories in legacy (Table 6) and a quarter on digital platforms (Table 9) relate to politics and government. Economy, science & health, and social & legal news enjoy comparatively equal share of the news space (17% each in traditional sources, 16-18% on News websites and News media Twitter feeds). Historically, approximately one in 10 stories on the main news pages and in newscasts has been about celebrity and sports, a pattern that continues today. 2 Social & legal news dominated in 2015 due to stories about the Germanwings plane crash in the Alps the day prior to the 2015 GMMP. 12 Table 6. Topics in the news. 2005-2020 Topic PRINT 2005 RADIO TV TOTAL PRINT 2010 RADIO TV TOTAL PRINT 2015 RADIO TV TOTAL PRINT 2020 RADIO TV TOTAL Politics and Government 27% 23% 23% 25% 32% 28% 26% 28% 26% 22% 21% 24% 25% 25% 21% 24% Economy 19% 27% 18% 21% 16% 21% 17% 17% 14% 18% 12% 14% 19% 18% 15% 17% Science and Health 11% 11% 9% 10% 10% 9% 9% 9% 9% 6% 8% 8% 14% 18% 18% 1//o Social and Legal 14% 11% 10% 12% 15% 12% 10% 13% 28% 27% 26% 27% 18% 15% 17% 17% Crime and Violence 20% 17% 22% 20% 19% 18% 22% 20% 12% 14% 15% 13% 11% 10% 13% 12% Gender & related - - - - - - - - - - - - 1% 1% 1% 1% CeLebrity, Arts and 9% Media, Sports 9% 8% 14% 10% 7% 10% 14% 11% 9% 12% 15% 11% 9% 8% 11% Other 0 1% 1% 2% 1% 1% 2% 2% 1% 2% 3% 2% 3% 5% 4% 4% The share of crime & violence news, that is, stories on murder, theft, corruption, war, child abuse and similar issues, has declined considerably over time from 20% in 2005 to 13% in 2015 and 12% at present. In 2020 a new category 'gender & related' was carved out to filter three types of stories: on sexual harassment against women, rape, sexual assault, #MeToo, #TimesUp; on other forms of gender violence such as feminicide, trafficking of girls and women, FGM; and, on inequality between women and men such as the gender pay gap. It was disappointing to find only 1% of stories coded under this new major topic in both legacy and digital media, yet, various sources indicated that gender violence has intensified during the pandemic. Overall, only 6 out of 100 stories are related to sexual harassment against women, rape, sexual assault, #MeToo, a proportion that flies in the face of reality; gender-based violence acquired a new moniker "the shadow pandemic" as the incidents rose by 30% across the world during the lockdowns imposed to contain the spread of the virus.3 The near absence of coverage of gender-based atrocities committed against girls and women further supports the observation that such acts have been normalized in and through media coverage. Examples of GBV stones during the global monitoring day Chile: "You are the rapist" According to the magazine "Time", four Chilean women are among the most influential people of 2020. They owe this to their activism against sexual violence and femicide. https://www.tagesanzeiger.ch/der-vergewaltiger-bist-du-775392656026 Ireland: "Teenager who admitted engaging in sexual activity with 14-year old girl banned from making contact with her" Judge says victim statement shows a "serious and significant downturn in her life" https://www.belfastlive.co.uk/news/belfast-news/teenager-who-admit-ted-engaging-sexual-19010894 3 https://www.unwomen.Org/-/media/headquarters/attachments/sections /libra ry/publications/2020/issue-brief-covid-19-and-ending-violence-against-women-and-girls-infographic-en.pdf ?la=en&vs=5348 13 India: "Hang the culprits... Bollywood stars demand justice for Hathras gangrape victim" Taking to Twitter, Akshay Kumar wrote that the incident had left him "angry and frustrated" and also called for the hanging of the rapists. https://zeenews.india.com/people/hang-the-culprits-akshay-kumar-far- han-akhtar-and-other-bollywood-stars-demand-justice-for-hathras-gan- grape-victim-2313362.html Mexico: They investigate sexual abuse of CDMX policewomen The Secretariat confronts the problem in a clear way, recognizes the Citizen Council and rules out that it is a generalized practice within the CDMX corporation https://www.eluniversal.com.mx/metropoli/cdrnx/investigan-abuso-sexu-al-mujeres-policias-de-la-cdmx Guyana: Port Kaituma man arrested after caught having sex with dead woman who tested positive for COVID-19 https://newsroom.gy/2020/09/29/port-kaituma-man-arrested-after-caught-having-sex-with-dead-woman-who-tested-positive-for-covid-19/ Pakistan: Motorway rape case: Victim woman ready to record statement https://twitter.corn/92newschannel/status/1310903999441375238?s=20 India: 19-year-old Dalit woman dies days after brutal gangrape in UP's Hathras Iceland: "You are disrespecting my daughter" An advertisement for the carbonated drink Kristal produced by Olgerflin has provoked a strong reaction in the Facebook group Feministaspjallifl. DV spoke to the mother of the young woman who is in the ad. https://www.dv.is/frettir/2020/9/29/modir-ungu-konunnar-kristals-aug-lysingunni-stigur-fram-thu-ert-ad-litilsvirda-dottur-mina/ 19-year-old Dalli woman dlea days aftai Erjlal gangrape in UPa Hb.It«5 Hwild IWl "W-VPi it in Cay» •<•* t»uH tjünjrjp» in I e-i H.,!t.-.,-. ~ttt K-y*tr-flk!Ort' w^, yi*o**s iwJbyt&s i\":n°'i'Jrj/3ta0t3J<'> ruirvaL ottir*n lucdimlHC [q n*v injuria mi lv**He/ raatrvH} äft* rua Mt The 19-year-old Dalit woman, who was raped by four men in Uttar Pradesh's Hathras district succumbed to her injuries https://twitter.eom/thenewsminute/status/l 310818994580893696 GMMP2020 14 Who Makes the News? Stories on domestic politics have habitually been most prominent on the news agenda and 2020 was no different. Due to the intense coverage of Covid-19, it is not surprising that articles on medicine and health rose to the top of the list of most reported news. (Table 7) To the extent that comparison is possible4, the top 10 sub-topics have remained relatively unchanged since 2005 except for the complete elimination of "environment & nature" from the list and a re-ordering of items on the hierarchy. The lack of variation in the leading sub-topics despite the diversity of issues and events present across the globe demonstrates perhaps fixity in the news media's prioritization of what stories are most newsworthy. Stories that were least visible in the major news of the global monitoring day were those about gender relations/roles and relationships of women and men, family law/inheritance law/rights, HIV and AIDS, birth control, family relations/single parents and informal work, sub-topics in the bottom 10 on the list, and issues that affect women disproportionately. Table 7. Top 10 news topics on the global monitoring day 29 September 2020 Rank Topic description 1 2 3 4 5 6 7 Other domestic politics/government (local, regional, national), elections, speeches, the political process... Medicine, health, hygiene, safety, disability, medical research, funding (not EBOLA or HIV-AIDS)... Economic policies, strategies, modules, indicators, stock markets, taxes Foreign/international politics, relations with other countries, negotiations,treaties, UN peacekeeping... Sports,events, players, facilities,training, policies, funding... Education, childcare, nursery, university, literacy Violent crime, murder, abduction, kidnapping, assault, drug-related violence... 8 Disaster, accident, famine, flood, plane crash, etc. 9 Legal system, judicial system, legislation (apart from family, property I inheritance law)... 10 War, civil war, terrorism, state-based violence Table 8. Topics in newspaper, television and radio news. Regional comparisons. 2020 Africa Asia Caribe Europe Latin America Middle East North America Pacific Politics and Government 30% 25% 21% 25% 21% 27% 25% 18% Economy 19% 21% 20% 16% 19% 14% 18% 20% Science and Health 10% 17% 18% 19% 17% 24% 23% 13% Social and Legal 23% 17% 20% 15% 17% 15% 17% 20% Crime and Violence 8% 11% 10% 14% 14% 13% 6% 10% Gender & Related 1% 2% 1% 1% 2% 0% 0% 0% Celebrity, Arts and Media, Sports 10% 6% 9% 11% 11% 6% 10% 18% Politics is the topic carried most in traditional news across all regions except for the Pacific where social & legal stories, and economic news are slightly more prominent. (Table 8) Politics, economy and social/legal news are among the top three major topics worldwide apart from in Europe, North America and the Middle East; in these three regions, science/health stories are second to politics in their share of the news space. The overall distribution of major topics in digital news matches patterns in legacy media, with one in four stories centred on politics/government, and social/legal, science/health and economic stories having more or less equal share (16-18%) of the news space. (Table 9) Africa is notable for the disproportionate focus on political news, accounting for 30% of the main news in traditional mediums and almost 40% of digital stories. In fact, a large proportion of African political stories were coded under the sub-topics "other domestic politics, elections, political process" and "peace, negotiations, treaties", issues that dominate public discourse on the continent. 4 The list of topics has expanded over time to incorporate emerging issues or to increase clarity on the themes covered under the respective themes. GMMP2020 15 Who Makes the News? Table 9. Topics on News websites and News media Twitter feeds news. Regional comparisons. 2020 Latin Middle North Africa Asia Caribe Europe America East America Pacific TOTAL Politics and Government 39% 28% 18% 23% 25% 22% 28% 25% 25% Economy 17% 18% 17% 15% 17% 14% 17% 18% 16% Science and Health 7% 14% 19% 18% 14% 25% 20% 18% 16% Social and Legal 21% 17% 21% 17% 17% 17% 16% 15% 18% Crime and Violence 8% 11% 11% 14% 14% 16% 10% 14% 13% Gender & Related 0% 2% 4% 1% 4% 0% 2% 1% 1% Celebrity.Arts and Media, Sports 8% 10% 10% 13% 9% 6% 8% 10% 10% CASE STUDY Brazil Trans women inmates of the Federal District CBN - Primeiras Noticias The story is about the achievement of trans women prisoners in the Federal District. Lasting approximately one and a half minutes, the news presents the case of a trans woman who was serving a sentence in a male penitentiary and won in court the right to be transferred to a female penitentiary. The court decision also favored all trans women who are in prisons in the Federal District (DF). The article has only an indirect quote from the trans woman who is, in fact, the main subject of the news. Although three more different sources related to the theme were heard, the trans woman is silenced yet she is the main character and only indirectly quoted. The other sources are: a soundtrack by the president of the LGBT Association commemorating the achievement; an indirect speech by the judge in the case that highlights the importance of respecting diversity; and another indirect citation of the minister's decision which determined that trans women be transferred to women's prisons. The information is largely by the reporter and the two female sources appear in the form of indirect quotes, showing an imbalance of space and visibility. Even though it is an individual demand of a trans inmate, the court decision favored the entire group of trans women in the area, thus, this in itself is an event that deserved to have been better explored and, therefore, to have had a longer duration within the newscast. The right achieved by this woman represents not only an achievement, but a protection for people who feel vulnerable within a male prison. With that, the story should have added at least one more voice from another trans woman who has also benefited from this decision. Not even the judge who presided the case is mentioned by her name. The two men (the president of the LGBT Association and the minister of the STF) are identified. Men are prominent in the story, to the detriment of female sources, who do not receive the same treatment. Despite the fact that the story is about an important achievement of trans women, the article features a man as a spokesperson for the LGBT community, and he is the only person interviewed. GMMP2020 16 Who Makes the News? Table 10. Key Findings: 1995 - 2020 1995 %F %M 2000 %F %M 2005 %F %M 2010 %F %M 2015 %F %M 2020 %F %M %Change (A) %F %M A. People in the news by Medium A25 yrs (%F) Newspaper, Television, Radio (NRT) 17 83 18 82 21 79 24 76 24 76 25 75 +8 Newspapers 16 84 17 83 21 79 24 76 26 74 26 74 +10 Television 21 79 22 78 22 78 24 76 24 76 26 74 +5 Radio 15 85 13 87 17 83 22 78 21 79 23 77 +8 A5 yrs (%F) News websites and news media tweets 26 74 27 73 +1 News websites 23 77 25 75 28 72 +3 (pilot) (pilot) News tweets 28 72 26 74 (-2) by Scope of Story. NRT A25 yrs (%F) Local 22 78 23 77 27 73 26 74 27 73 29 71 +7 National 14 86 17 83 19 81 23 77 23 77 25 75 +11 National/other 17 83 15 85 18 82 20 80 Sub-regionaL/regionaL (1) 24 76 24 76 International / Foreign 17 83 14 86 20 80 26 74 24 76 21 79 +4 By Major topic. NRT Science & Health 27 73 21 79 22 78 32 68 35 65 30 70 +3 Social & Legal 19 81 21 79 28 72 30 70 28 72 32 68 +13 Crime & Violence 21 79 18 82 22 78 24 76 28 72 24 76 +3 CeLebrity, Arts & Sport 24 76 23 77 28 72 26 74 23 77 25 75 +1 Economy 10 90 18 82 20 80 20 80 21 79 24 76 +14 Politics & Government 7 93 12 88 14 86 19 81 16 84 20 80 +13 by Function in Story. NRT A15 yrs (%F) Personal Experience 31 69 36 64 38 62 42 58 +11 Popular Opinion 34 66 44 56 37 63 38 62 +4 Eye Witness 30 70 29 71 30 70 30 70 0 Subject 23 77 23 77 26 74 24 76 +1 Spokesperson 14 86 19 81 20 80 22 78 +8 Expert 17 83 20 80 19 81 24 76 +7 by Occupation. NRT A15 yrs (%F) Homemaker, parent (no other occupation is given) 81 19 75 25 72 28 67 33 68 32 (-7) Health worker, social worker, childcare worker n/a n/a n/a 47 53 47 53 Office or service worker, non-management worker 35 65 40 60 45 55 35 65 42 58 +2 Unemployed no other occupation given 33 67 19 81 35 65 34 66 42 58 +23 Activist or worker in civil society org., NGO, trade union 24 76 23 77 34 66 33 67 35 65 +12 Doctor,dentist, health specialist n/a n/a n/a 30 70 29 71 Academic expert, lecturer, teacher n/a n/a n/a 23 77 29 71 Lawyer, judge, magistrate, legal advocate, etc. n/a 18 82 17 83 22 78 25 75 +7 Media professionaI, journalist, film-maker, etc. n/a 36 64 29 71 21 79 29 71 (-7) Tradesperson, artisan, labourer, truck driver, etc. 15 85 23 77 22 78 21 79 21 79 (-2) Government employee, public servant, etc. 12 88 17 83 17 83 20 80 22 78 +5 Government, politician, minister, spokesperson.. 10 90 12 88 17 83 18 82 18 82 +6 Business person, exec, manager, stock broker... 12 88 14 86 16 84 20 80 +8 Agriculture, mining, fishing, forestry 15 85 13 87 13 87 14 86 24 76 +11 Science/technology professional, engineer, etc. 12 88 10 90 10 90 10 90 20 80 +10 Police, military, para-military, militia,fire officer 4 96 5 95 7 93 8 92 12 88 +7 Sportsperson, athlete, player, coach, referee 9 91 16 84 11 89 7 93 14 86 (-2) GMMP2020 17 Who Makes the News? 1995 %F %M 2000 %F %M 2005 %F %M 2010 %F %M 2015 %F %M 2020 %F %M %Change (A) %F %M A20 yrs % Portrayed as Victim. NRT 29 10 19 7 19 8 18 8 16 8 14 15 (-5) +8 % Portrayed as Survivor. NRT 4 8 6 3 8 3 6 7 % Identified by Family Status. NRT 21 4 17 5 18 5 19 5 14 5 (-7) +1 % In Newspaper Photographs 25 11 23 16 26 17 30 23 27 24 +2 +13 % Quoted. NRT 33 35 50 50 52 50 61 61 57 55 +24 +20 B. Reporting and Presenting the News A20 yrs %F % Stories presented 51 49 49 51 53 47 49 51 49 51 51 49 +2 Television 56 44 57 43 52 48 57 43 55 45 -1 Radio 41 59 49 51 45 55 41 59 46 54 +5 % Stories reported 28 72 31 69 37 63 37 63 37 63 40 60 +9 Television 36 64 42 58 44 56 38 62 45 55 +9 Radio 28 72 45 55 37 63 41 59 37 63 +9 Newspapers 26 74 29 71 33 67 35 65 37 63 +11 % Stories reported in digital news 42 58 News websites 42 58 News media Twitter feeds 43 57 % Stories reported, by scope, by sex of reporter. NRT A25yrs(%F) Local 33 67 34 66 44 56 40 60 38 62 40 60 National 24 76 30 70 34 66 38 62 38 62 41 59 +17 National/other 28 72 33 67 32 68 32 68 Sub-regional/regional 37 63 40 60 Foreign/International 28 72 29 71 36 64 37 63 35 65 38 62 +10 % Stories Reported By Major Topic. NRT A20yrs(%F) CeLebrity, Arts & Sport 27 73 35 65 38 62 33 67 40 60 +13 Social & Legal 39 61 40 60 43 57 39 61 44 56 +5 Crime & Violence 29 71 33 67 35 65 33 67 33 67 +4 Science & Health 46 54 38 62 44 56 50 50 49 51 +3 Economy 35 65 43 57 40 60 39 61 41 59 +6 Politics & Government 26 74 32 68 33 67 31 69 35 65 +9 % Female news subjects and sources, by sex of reporter. NRT 24 18 25 20 28 22 29 26 31 24 +7 C. News Content A20 yrs % Stories with Women as a Central Focus. NRT* 10 10 13 10 9 (-1) CeLebrity, Arts & Sport 16 17 16 14 L3 (-3) Social & Legal 19 17 17 8 L2 (-7) Crime & Violence 10 16 16 17 L4 +4 Politics & Government 7 8 13 7 7 O Science & Health 11 6 11 14 4 (-7) Economy 4 3 4 5 4 O A15 yrs % Stories that Challenge Gender Stereotypes. NRT 3 6 4 3 0 % Stories that Highlight Gender (In)EquaLity. News websites and tweets 4 % Stories that Highlight Gender (In)EquaLity. NRT 4 6 9 7 +3 % Stories that Highlight Gender (In)EquaLity. News websites 8 A10 yrs % Stories that mention gender eguality policies or human/women's rights instruments. NRT 10 9 7 (-3) GMMP2020 18 Who Makes the News? 1995 2000 2005 2010 2015 2020 %Change (A) _%F %M %F %M %F %M %F %M %F %M %F %M %F %M COVID-19 News (ALL mediums) All subjects and sources 28 72 Experts 26 74 Doctors, health specialists 27 73 Reporters 48 52 Stories that clearly challenge gender stereotypes 2 Stories that highlight gender ineguality issues 5 Notes 'Sub-regionaL/regionaL' category replaced 'national and other in 2015 Empty cells mean data collected for the respective indicator * Correction: A data capture error resulted in under-counting the 'yes' responses for the indicator"% of stories in which women are central" in the earlier published report This version shows the corrected finding. Table 11. Major topics by space in newspapers. 2020 FuLL page % page % page 14 page Less than % page Politics and Government 23% 25% 24% 22% 27% Economy 20% 22% 19% 19% 16% Science and Health 14% 13% 14% 15% 14% Social and Legal 20% 16% 20% 19% 16% Crime and Violence 9% 9% 11% 11% 14% Gender & Related 1% 1% 1% 1% 1% CeLebrity, Arts and Media, Sports 11% 10% 7% 7% 8% Other 2% 3% 3% 5% 4% Political stories are more likely to occupy more space on newspaper pages than any other topic, whether an entire page, half, one third or a quarter of the page. (Table 11) The greater the news value that newsroom decision-makers attribute to a story, the more likely that it will be given prominence in space and placement in the newspaper, news broadcast or website. Stories considered to be more important are likely to be longer and appear on the front pages, home page or in the initial segments of a newscast. Almost 50% of the stories coded under the 10 least prominent sub-topics that also affect women disproportionately were accorded a quarter or less than a quarter of a print news page. GMMP Monitoring Day: Dakar GMMP2020 19 Who Makes the News? News subjects and sources: Progress without revolution ■1 88 Gender Gap: News Subjects & Sources (2020) 18 Data Source: GMMP 2020 Created with Datawrapper General patterns To quote Wright (2011), to look for "revolution" is to overlook the significance of incremental change. For the first time since 2010, there appears to be a slight upward movement in the proportion of women as sources and subjects in the news, notably in broadcast media. While the overall increase is only one point, it is nevertheless statistically important (p<.001) and edges the needle in the right direction half-way to equality. (Table 12) 5Q2£ 1 Gender Equality Women subjects and sources by medium. All things remaining equal, it will take at least a further 67years to close the average gender equality gap in traditional news media. 1995 2020 2087 GMMP 2020 20 Who Makes the News? Table 12. Women subjects and sources by medium. 1995-2020 1995 2000 2005 2010 2015 2020 A25yrs NEWSPAPER 16% 17% 21% 24% 26% 26% +10% RADIO 15% 13% 17% 22% 21% 23% +8% TELEVISION 21% 22% 22% 24% 24% 26% +5% Total 17% 18% 21% 24% 24% 25% +8% European news media have made the most significant progress on this indicator since 1995 and Pacific region media in the past five years. Only Africa's media on average have stagnated as the rest of the regions have improved by three to 12 points across the quarter century. Despite a three-point decline in the proportion of women subjects and sources since 2015, North American news media remain the best performers worldwide. Table 13. Overall presence of women in print, radio and television news, by region. 1995-2020 Region 1995 2000 2005 2010 2015 2020 A 25 yrs Africa 22% 11% 19% 19% 22% 22% 0% Asia 14% 17% 19% 20% 20% 21% +7% Caribbean 22% 24% 25% 25% 29% 27% +5% Europe 16% 19% 21% 26% 25% 28% +12% Latin America 16% 20% 23% 29% 29% 26% +10% Middle East 14% 15% 15% 16% 18% 17% +3% North America 27% 25% 26% 28% 36% 33% +6% Pacific' 20% 25% 26% 25% 26% 31% +11% GLOBAL AVERAGE 17% 18% 21% 24% 24% 25% +8% * Sample drawn from four nations which are also the most populous, namely Australia, Papua New Guinea, New Zealand and Fiji where over 90% of the region's population reside. The proportion of women as subjects and sources in digital news stories increased one point as well from 2015 to 2020, with a 3-point improvement on news websites and a mirror 3-point decline in news media tweets. The professional news space on social media is trending towards increased exclusion of women as subjects and sources. The overwhelming majority of science/health news (66% in traditional medium, 65% in all mediums combined) was related to Covid-19, the limelight story of 2020. Not only did this topic's share of the news space increase considerably (more than doubling since 2015), men's visibility as persons in this set of stories rose as well. It maybe assumed that the gender gap widened due to the recorded higher men's virus-related mortality rates thus increasing the proportion of men as story subjects. However, taking the example of the Web-published science/health news sample, men appeared in the stories overwhelmingly as opinion givers (65%) rather than as persons whom the stories were about (35%), in contrast to women's lower presence as information sources (57%) and higher as subjects (43%). Consistent with historical patterns, women are still least likely to appear in political stories in traditional (Table 14) and digital (Table 17) news outlets. They are fewer than two in 10 of the people in this topic in Africa, Asia and the Middle East offline (Table 15) and online (Table 17), and over 30% in the Pacific region across the five media types. GMMP2020 21 Who Makes the News? Table 14. Overall presence of women in print, radio and television news, by major topic, by GMMP year. 1995-2020 1995 2000 2005 2010 2015 2020 A 25 years Politics and Government 7% 12% 14% 19% 16% 20% +13% Economy 10% 18% 20% 20% 21% 24% +14% Science and Health 27% 21% 22% 32% 35% 30% +3% Social and Legal 19% 21% 28% 30% 28% 31% +12% Crime and Violence 21% 18% 22% 24% 28% 24% +3% Gender & Related 47%* CeLebrity, Arts and Media, Sports 24% 23% 28% 26% 23% 25% +1% 'Gender & related N=739,1% of total sample Women's overall presence in the news in North America and Pacific has surpassed the critical 30% threshold in both digital and legacy media. On this indicator, Africa falls below the global averages across all media types monitored, as do Asia and the Middle East in print and broadcast news. The Middle East is particularly troubling, with women being fewer than two out of 10 persons seen, heard or read about in traditional news media. The Caribbean region crosses the 30% mark on women as subjects and sources in stories published on news websites and tweeted by news media outlets. In traditional media, women are more likely to appear as sources and subjects in social/legal, science/health and crime/violence major topics everywhere5 in which women are most likely to be featured worldwide except for the Middle East; in this region, the level of women's presence in social/legal news matches that found in celebrity news. Women are at least four out of 10 subjects and sources in Caribbean, North American and Pacific social & legal news. These are stories about education, migration, human rights, riots, activism, family law and similar topics. In Asia, women's voice and visibility in celebrity/sports matches the same in science/health and social/legal news. Table 15. Women subjects and sources in print, radio and television news, by major topic, by region. 2020 Latin Middle North Africa Asia Caribbean Europe America East America Pacific OVERALL Politics and Government 18% 15% 21% 22% 20% 12% 26% 32% 20% Economy 19% 21% 23% 29% 24% 12% 36% 27% 24% Science and Health 30% 25% 28% 35% 28% 15% 36% 33% 30% Social and Legal 23% 24% 42% 34% 34% 23% 45% 40% 31% Crime and Violence 24% 22% 26% 26% 24% 19% 29% 30% 24% Gender & Related 66% 31% 67% 58% 51% 75% 50% 58% *47% CeLebrity, Arts and Media, Sports 17% 25% 20% 30% 21% 24% 26% 21% 25% OVERALL 22% 21% 27% 28% 26% 17% 33% 31% 25% '*'!% of total sample 5 Major topic gender & related' is left out of the analysis for reasons of the negligible sample size and the obvious women-focus of the sub-topics which make for an expected over-re presentation of women as subjects and sources. GMMP 2020 22 Who Makes the News? There are no more than two women for every 10 people in political stories in traditional and digital news in the Middle East, Asia, Africa, and Latin America. Women's presence in this genre of stories across all mediums is highest in the Pacific region and lowest in the Middle East. Voice and visibility in economic stories are just as dismal in the Middle East, Asia, and Africa across traditional and digital delivery platforms. The only region and topic in which gender parity in subjects and sources has been attained is in North American digital social & legal news. Online, social & legal news is among the top three major topics in which women are most likely to be present except for Africa and the Pacific; in Africa, women are more likely to be seen, heard and read about in crime & violence news, next to the science/health major topic. One in three persons in African crime news is a woman although this level of visibility is common more or less to many regions: 33% in the Pacific, Caribbean (32%), North America (31%), the lowest being in the Middle East at 23%. Crime and violence stories here include those on topics such as theft, drugs, corruption, murder and war. Transnational media Transnational media performed poorly with regard to inclusion of women as sources and subjects. Women were only 13% of sources and subjects in the television newscast monitored and 21% in the digital news stories and tweets coded from Al Jazeera, BBC News -World, CNN International, France 24, Reuters, RT News, TeleSur, @ny-times. (Table 16) Women were 15% of the people in digital transnational stories in the slightly larger sample coded in 2015. Though the results would need to be replicated in a repeat study with a larger sample of stories, they indicate a continuation of the general pattern of women's invisibility in influential international media that serve formidable audiences, yet fall short of their responsibility to observe the industry and in-house ethics codes, to report fairly and truthfully to the highest professional standards, and to reflect the gender diversity of the audiences they serve. Table 16. Overall presence of women in transnational news sites. 2020 CASE STUDY Bolivia Citizens call for a mobilization in repudiation of the sentence of William Kushner. Item in Radio Panamericana newscast Women Men n Sitel 20% 80% 45 Site2 17% 83% 18 Site 3 29% 71% 48 Site4 13% 87% 53 Site 5 33% 67% 33 Site6 25% 75% 12 Site7 23% 77% 26 Interestingly, while the news share of science/health stories was significantly higher in 2020 compared to earlier periods (from 10% in 2005, to 9% in 2010, 8% in 2015 and 17% currently), women's presence in this topic has declined by five points after a steady rise between 2000 and 2015: (Table 14) The meteoric climb in the topic's news worthiness due to the pandemic has been accompanied by a fall in women's voice and visibility in the stories. By generalising the action to 'citizens', the story title gives the impression that the entire population or a large part of it is against the sentence of William Kushner who was convicted of femicide. The story implies that the act was not a femicide, but an accident. Only one woman is interviewed who is a relative of Kushner, acting as "spokesperson" and defender of the aggressor. The people who organized the march should have been interviewed. The point of view of the other party was also necessary and especially due to the accusations made by Kushner's relative who stated: "It seems that the sentence was written much earlier (...) the audit is key, there must be a higher body that controls what these judges and these lawyers have done ". The story is topical, however, it has numerous gaps in terms of perspective, the single source that speaks, terminology, and the generalization in the headline. GMMP2020 23 Who Makes the News? Canada: Que. nurse fired, coroner to investigate after dying US: "Barrett's Life Inspires Conservative Women" by Ruth Indigenous woman taunted in hospital Graham, (Print) New York Times https://montreal.ctvnews.ca/que-nurse-fired-coroner-to-investigate-af-ter-dying-indigenous-woman-taunted-in-hospital-1.5125145 Nigeria: "15-year-old girl flees Plateau to escape child marriage, rescued by FIDA Ekit" https://www.pulse.ng/news/local/15-years-old-girl-flees-plateau-to-es-cape-child/marriage/2n5f92v The story is clearly about the appeal of Judge Amy Barrett to religiously conservative women. It is a personal as well as partisan political appeal. This gender-specific story positions an angle that lifts up the ideal of womanhood depicted as one of a large family, in this case 7 children (2 among them adopted), with a high professional achievement in law. If it were a male nominee, the number of children or the combination of a large family and professional achievement would not be a gender indicator for success. Usually, the question posed is whether he would stand for planned parenthood or pro-life when it comes to taking a legal stance. While this story lifts up the model of a woman who has defied the saying, "biology is destiny," such a narrative leaves out other categories that intersect with gender such as health care, medical access, education, child care, and economic security. None of the women interviewed are from a lower socio-economic status. All the interviewees have had access to higher education. Seen only through the prism of religion, reproduction, and profession, the image and representation of women tend to lack nuanced understandings of gender. What would it be to have a news-media world where the portrayal of female success and that of male success were measured by the same metrics! Gender-just metrics! https://www.nytimes.com/2020/09/29/us/amy-coney-barrett-meets-with-sen-ator-mitch-mcconnell-who-hopes-to-speed-her-confirmation-to-the-supreme-court.html Table 17. Women subjects and sources in content on news websites and news media tweets, by major topic, by region. 2020 Latin North Africa Asia Caribbean Europe America Middle East America Pacific OVERALL Politics and Government 16% 19% 24% 23% 20% 14% 29% 33% 21% Economy 22% 20% 18% 27% 24% 13% 32% 28% 23% Science and Health 46% 30% 38% 32% 32% 19% 39% 40% 31% Social and Legal 21% 31% 37% 33% 34% 30% 50% 25% 32% Crime and Violence 33% 25% 32% 25% 28% 23% 31% 33% 27% Gender & Related 20% 42% 79% 59% 50% - 54% 57% *53% CeLebrity, Arts and Media, Sports 24% 39% 23% 35% 32% 17% 40% 34% 33% OVERALL 22% 26% 31% 28% 27% 20% 37% 33% 27% '-'denotes nil stories coded under this topic in the region of total sample 24 Gender and related: Who makes #MeToo news? This major topic carved out for the 2020 GMMP covers three sets of gender-specific stories: on sexual harassment against women, rape, sexual assault, #MeToo and similar sub-topics; on gender violence such as feminicide, trafficking of women and girls, FGM; and, on inequality between women and men. The past five years have seen an intensification of feminist activism against violence against women (YAW), as of gender, media and communication scholarship where analysis has largely considered the mainstream media's coverage of the #MeToo movement.6 Numerous studies analyze how the news media frame the movement or VAW overall, others critique failures to problematize the seeming exclusionary nature of the movement, yet others centre on journalistic ethics and responsibility in VAW reporting. Within the literature on framing are those studies analyzing whether VAW is framed as an individual or societal/ structural problem (Hernandez 2017; Sutherland et al. 2019; Rojas Rajs 2014; Owusu-Addo et al. 2018; Se-la-Shayovitz 2018; O'Boyle and Li 2019; Bloomfield 2019; Nilsson 2019), whether the narrative serves to legitimize or dismiss the movement (Askanius and Hartley, n.d.), and media depiction of #MeToo as a natural force with local manifestations across the globe (Starkey et al. 2019). In the literature are critical insights on #MeToo coverage that supports "feminism alongside a concurrent de-polit-icization, an individualizing tendency through a focus on celebrity and the cultural industries, and the centering of the experiences of celebrity female subjects" who are "predominantly white and wealthy" (De Benedictis, Orgad, and Rottenberg 2019). Various other studies problematize the media centeredness on women who are privileged by race and/or class: Baker, Williams, & Rodrigues' (2020)#metoo 2.0 reinforces the gendered sexual violence in the creative sector [Marghitu, 2018. ?It?s Just art: Auteur Apologism in the Post-Weinstein era?, Feminist Media Studies, 18(93 review of Western coverage of sexual violence in the music industry finds a focus on affluent white women while less than 10% of the reports discussed the techno-legal dimensions of the movement; Tambe's (2018) review of American media coverage finds a focus on white women's stories and pain while in fact, sexual harassment and rape are "a pervasive workplace experience for women of color [...] as the viral reach of the [#MeToo] hashtag around the globe [...] makes clear". Mishra's (2020) analysis of Indian newspaper reports found a timeline that began with international stories, to stories of Indians living abroad, and later to issues in India largely focussed on celebrities and silent on the struggles of less powerful women. The studies suggest that marginalization of sexual violence survivors based on their race-, class-, and other social identities is common to #MeToo-focused journalism everywhere. Some studies focus on media practice, the extent to which 6 Started in 2007 by African American activist Tarana Burke as a grassroots movement to aid sexual assault survivors in underprivileged communities. CASE STUDY Bosnia & Herzegovina Who are the richest women in the world (Ko su najbogatije žene na svijetu) "All of them inherited a vast part of their wealth from either ancestors or ex-husbands" Published in Newspaper Dnevni avaz i ftosunajüooarue zene m suijeiu tm ~ ' ~ iiiii The article is about three women who are allegedly the richest women in the world. Smiling photos of the three take up one third of the space. The opening sentence states that none of those women ended up on the list of the richest women or has become one of the richest women in the world due to their work, but because they inherited all the wealth either from their ancestors or their ex-husbands. The story's overall message trivialises and objectifies women by stating that a woman can become rich only if she marries a rich man or is born into a wealthy family. GMMP 2020 25 Who Makes the News? journalists exercise accountability to their sources; as Foster & Minwalla (2018) argue, "that journalists, editors, and large multi-media conglomerates are failing to consider the risks they expose their sources to when they disregard ethical guidelines [...] speaks to the need to further investigate the economic, political, and institutional contexts in which media organizations openly or tacitly encourage reckless conduct, and to the need for media consumers, themselves, to organize, for media accountability". Hindes & Fileborn's (2020) study concluded that the majority of reporting on sexual violence "still perpetuated limited and binary understandings of sexual violence. Much reporting constructed pressure and coercion as the normal and acceptable 'reality' of (hetero)sex, failing to acknowledge coercion as potentially harmful and problematic, as well as failing to consider the possibilities for doing consent differently". Researchers have put a finger on the disregard of women as sources in VAW media stories, noting the over-reliance on law enforcement officers (Sutherland et al. 201 ^therefore, aimed to establish a baseline picture of the extent and nature of reporting of violence against women by the mainstream Australian news media. Methods: Descriptive and content analysis of media reports on violence against women that were collected over four months in three states of Australia. Reports were from newspapers, broadcast (television and radio, powerful men and third party entities who are not directly involved in the event (Field, Bhat, and Tsvetkov 2019). So, while the movement empowers women to speak out, this empowerment is not translated to voice in media stories about them. In general, there is agreement that story angles, frequency of reporting, information included or omitted count in shaping societal views of violence against women and preventing it. Just 1% of the stories in the GMMP 2020 sample were coded under the gender and related major topic, distributed across the sub-topics "sexual harassment against women, rape, sexual assault, #MeToo #TimesUp" (54% in traditional news, 40% on websites, 39% on Twitter), "Other gender violence such as feminicide, trafficking of girls and women, FGM..." (39%, 54% and 54% respectively), and "Inequality between women and men such as income inequality/gender pay gap" (7 %, 5%, 7% for legacy media, news website and news media Twitter content respectively). (Table 18) Table 18. Gender and related news sample, percent distribution within major topic by media type. 2020 News websites and Print, radio, television news media tweets Sexual harassment against women, rape, sexual assault, #MeToo #TimesUp... 54% 40% Other gender violence such as feminicide, trafficking of girls and women, FGM... 39% 55% Inequality between women and men such as income inequality/gender pay gap... 7% 5% Total 100% 100% 46% of stories coded under the topic "sexual harassment, rape, sexual assault, #MeToo and similar" were obtained in Asian news, specifically India, Bangladesh, Nepal and Pakistan. 60% of those coded under "other gender violence, such as feminicide, trafficking of girls and women, FGM and similar" were from the Latin American sample. Overall, the largest crop of stories under the broader topic "gender & related" grouping both sub-topics and a third on gender-specific news were from Latin America (40%) followed by Asia (26%), Europe (18%), Caribbean (8%) and Africa (4%). The underrepresentation of girls and women in stories about sexual harassment, rape and sexual assault particularly now, during Covid-19 times when such acts have reached pandemic proportions7, signals the news media's lack of accountability to and respect for women. This silencing of women continues a pattern detected in the 2005 GMMP which concluded that women did not make the news even in stories that concerned them more. In print and broadcast news, women were only 4 out of 10 subjects and sources in stories on sexual harassment, 5 in other gender violence articles, and 7 in news specific to gender inequality. Comparing results on print, online and televised stories on various forms of gender violence, the most severe underrepresentation of women in sexual harassment/rape/#MeToo/sexual assault stories is in newspapers (Table 19); in print, women are 35% of subjects and sources. The mediums perform better in granting visibility to women in stories on other forms of gender violence news such as feminicide and trafficking of girls and women. Further, while boys and men are overwhelmingly the subjects and sources in rape and sexual harassment stories, only 10% of them are portrayed as victims or survivors compared to 58% of women. In stories on other forms of gender violence, 3% of the boys and men present are presented as victims or survivors compared to 24% of the girls and women. In the Latin American monitoring, the question was answered whether the person in the story was identified as a perpetrator; in this region, men were 86% of the people in GBV stories and 100% of those in #MeToo and related news mentioned as perpetrators. In relation to the accusations against Donald Trump of sexual violence and misogyny, Blumell (2019) found that female sources were more likely to defend survivors and not Trump, while the opposite was true for males. Television media used male sources significantly more than print and online media, while also using female sources less; print media uses male sources significantly more than on- 7 A literature review found consistent patterns of rise in gender-based violence during Covid-19 and past pandemics (Mittal and Singh 2020) GMMP 2020 26 Who Makes the News? line, but there is no significant relationship between their use of female sources. Television had significantly higher levels of Trump defense, while online media had significantly more survivor defense. However, other scholars have illuminated some of the ways sourcing practices could be improved. Hollings (2020) found that New Zealand's Stuff's survivor-led approach to covering #MeToo was effective for both the survivors themselves as well as the journalists covering the cases. Simons and Morgan (2018) reveal through two Australian newspapers that relying on both police who have shifted their views on sexual assault and social media as sources can lead to sexual violence being framed as a societal problem rather than isolated events. However, the authors fear that the lack of violence against women advocates driving the news agenda means that these changes will not be sustained. The literature reveals that under-representation of women's voices and unjust portrayal in stories on sexual violence (and other issues specific to women) are not uncom- mon. Bridges & Wadham (2020)exploring three categories (1 examined how women in the military were portrayed between 1997 and 2017 in two influential Australian newspapers. Almost 40% of the stories were about "the Skype Affair" in which a male cadet streamed himself having sex with a female cadet. "While discussion of military women in the media spiked [after the crime], their actual voices did not; 75% of articles in both newspapers did not include female sources at all". The gender of source in gender violence stories matters, as Blumell (2019) found in her analysis of stories in American cable television stations, national newspapers, and the most shared online articles related to the release of a recording of former U.S. President Donald Trump's conversation about grabbing women by their genitals: "...not only did female sources defend survivors more than male sources, there was a negative relationship with male sources and defending survivors. Conversely, male sources defended Trump significantly, while female sources did not". Table 19. Reporting on gender-based violence, subjects and sources, % women, by region. 2020 News websites Newspapers Television GLOBALAVERAGE 44% 35% 42% Africa 0% 61% 100%' Asia 34% 25% 40% Caribbean 50% - 100% Europe 57% 54% 29% Latin America 37% 53% 43% Middle East - 75% - North America 50% - - Pacific Islands 43% 58% Sexual harassment against women, rape, sexual assault, #MeToo #TimesUp Other gender violence such as feminicide,trafficking of GLOBALAVERAGE 61% 56% 48% girls and women, FGM... Africa 50% 50% 50% Asia 33% 40% 100%" Caribbean 87% 67% 60% Europe 59% 82% 47% Latin America 58% 50% 47% Pacific Islands 83% -denotes nil stories coded in the respective mediums and regions * less than five people coded GMMP2020 27 Who Makes the News? Women from minority and historically marginalized groups Teams in 81% of the participating countries took the opportunity provided by GMMP 2020's data collection instruments to define up to three special questions of interest in the national context that would allow for unpacking the results using intersectional lenses. The teams integrated a range of indicators pertaining either to the other identities of the persons in the stories, from disability, to race, immigration status, religion, class/caste, sexuality and various others, or to other dimensions of the story such as reference to a specific social justice movement such as Black Lives Matter. In some cases, the issue of interest was shared across countries and teams applied a collaborative approach to define and agree on their special questions. This was the case in the Latin American region where coders responded to the same three questions set collectively by the research leads. Where the coding found a complete absence of the dimension of interest, the results provide information about the invisibility of the issue on the news agenda. Table 20 shows the grouped results on shared indicators regarding the identities of the people in the stories. A comparison of the GMMP findings against the physical world statistics suggests that women are underrepresented across all the identity groups. The groups are in them- selves all underrepresented to different degrees except for racialized groups where the picture is mixed. In the UK sample, coders responded to the question "Is the person from an ethnic minority (not White British) background, either visibly or mentioned in the text?". In the Netherlands, the question was "Is de persoon een persoon van kleur, te zien in beeld of genoemd in de tekst? (Is the person a person of color, as seen in images or mentioned in the text?)." In Malta, coders answered the question "Is the person from an ethnic minority (not Maltese) background, either visible or mentioned in the text?". These questions were answered in the affirmative for 7% of people in British news, 10% in Maltese media and 16% in Dutch news on the global monitoring day. In the Dutch sample the minorities were present to comparable degrees in all major topics and in Malta they were most visible in celebrity/me-dia/sports, and social/legal news. In the UK however, ethnic minorities were 4% to 7% of subjects and sources in all major topics except for crime news where they constituted 14% of the people in the stories. Also in the UK sample, women were 30% of those coded as ethnic minorities and three out of 10 of minorities in crime/violence news. Table 20. News subjects and sources from minority and historically marginalized groups. 2020 % all sources and subjects % women n Indigenous, tribal, ancestral peoples (1) 3% 20% 277 Ethnic minorities, racialized groups, persons of colour, religious minorities (2) 7% 28% 279 Persons with disabilities)?) 8% 40% 205 Refugees, immigrants(4) 3% 18% 79 Notes 1 Coded in Argentina, Bolivia, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Mexico, Nicaragua, Paraguay, Uruguay and Venezuela. In perspective: Indigenous peoples are estimated at 8% of the Latin American regional population (World Bank) 2 Coded in England, Ireland, Malta, Netherlands, Niger, Romania, Scotland, Serbia and Wales. In perspective: About 14% of UK's population is non-White (gov.uk),and 10% tol4% in The Netherlands from non-European ethnic groups (CIA factbook) 3 Coded in Cambodia, Portugal, Turkey and the USA. In perspective: The global population living with a disability is estimated to be between 10% (Disabled World) to 15% (World Health Organisation). Women have higher rates of disability than men in OECD countries (Disabled World) and are 75% of the persons with disabilities in low and middle income countries (Human Rights Watch) 4 Coded in Cyprus, Israel, Portugal, Romania and Serbia. In perspective. The EU hosts about 10% of all the world's refugees and internally displaced persons (European Commission), over 50% of who are women (Migration Data Portal) See resources for media on reporting on refugees and migrants https://waccglobal.org/resources/ migration-reporting/ These particular data tell us that women are multiply marginalised based on their subordinate identities of gender, race, ability, and legal status in the respective contexts. Where they are visible, they make the news in very specific stories, exemplified by their preponderance in Britain's crime stories or Malta's celebrity news. The data show that people with disabilities are significantly under-represented everywhere they were coded. GMMP 2020 28 Who Makes the News? Figure 2. Indigenous women as a proportion of indigenous peoples in Latin American news 13%] 22% 21% 13% Newspapers Radio Television Twitter news News websites In Latin America only 3% of the people in the news are from indigenous or tribal groups and of these only one in five is a woman. In the physical world, however, indigenous peoples are estimated to be at least 8% (World Bank, 2015) of the region's population, at least 50% women. The breakdown by medium (Figure 2) indicates that the women are more likely to be present in stories published on news websites and are least visible in print and Twitter news. More broadly, the data demonstrate that the marginali-sation of women across the news agenda, in legacy media as much as in the newer digital platforms, is not the only problem when it comes to hearing, seeing and reading diverse voices. They demonstrate the importance of taking an intersectional approach when considering whose point of view is privileged by media professionals, since the further away that voice is from the non-disabled male majority, the more silenced it becomes. Thus, the democratic deficit made explicit from the baseline statistics already discussed in this report in relation to women's broader (in) visibility in news discourse is further exaggerated when additional elements such as ethnicity, (dis)ability and citizenship status is added to the mix. Of course, these intersectional data are likely to be considerably under-reported since not all disabilities are visible, not all ethnicities are an observably "minority" in their particular national context. Indeed, the issue of marginalised indigenous voices illustrates the power of political elites to control the news agenda. If we add in a few more personal characteristics such as age, we then start to comprehend the exclusive nature of sources, the narrow optics through which the world is observed. The failure to extend the opportunity for more citizens to tell their own stories in their own words, to tell the stories which are important to them and, also, to a broad range of people, compromises the value of the news to its multiple and diverse publics. Table 21. Top 10 topics* in which women are most Likely to be present in print, television and radio news. 2020 Rank Topic Rank Topic 1 2 3 4 5 Women's participation in economic processes (informal work, paid employment, unemployment, unpaid labour) Beauty contests, models, fashion, beauty aids, cosmetic surgery... Women's movement, feminist activism, events, demonstrations, gender eguality advocacy... Birth control, fertility,sterilization,amniocentesis,termination of pregnancy... Other gender violence such as feminicide, trafficking of girls and women, FGM... 6 7 8 9 10 Family law, family codes, property law, inheritance law and rights... Chi Id abuse, sexua I violence against chi Idren, neg lect Family relations, inter-generational conflict, single parents. HIV and AIDS, incidence, policy, treatment, people affected.. Sexual harassment against women, rape, sexual assault, #MeToo #TimesUp 'Excluded from this ranking are topics with less than 30 people in the stories, namely: Inequality between women and men'(n=29),'Changing gender relations, roles and relationships of women and men inside and outside the home' (n=27). See the complete list of topics in Table 23. GMMP2020 29 Who Makes the News? Table 22. Women's presence in news topics in print, television and radio news ...the bottom 10.2020 Rank Topic 1 2 3 4 5 War, civil war, terrorism, state-based violence Sports, events, players, facilities, training, policies, funding... EBOLA, treatment, response... National defence, military spending, military training, military parades, internal security... Foreign/international politics, relations with other countries, negotiations, treaties, UN peacekeeping... Rank Topic 6 7 8 9 10 Peace, negotiations, treaties... (local, regional, national), Riots, demonstrations, public disorder, etc. Economic policies, strategies, modules, indicators, stock markets, taxes,... Other domestic politics/government (local, regional, national), elections, speeches, the political process... Other labour issues, strikes, trade unions, negotiations, other employment and unemployment Table 23. Women as news subjects in different story topics in print, television and radio news. 2020. Topic % Women N Women's participation in economic processes (informal work, paid employment, unemployment, unpaid labour) 73% 147 Inequality between women and men such as income inequality/gender pay gap, 72% 29 Beauty contests, models, fashion, beauty aids, cosmetic surgery... 69% 71 Women's movement, feminist activism, events, demonstrations, gender equality advocacy... 66% 214 Birth control, fertility, sterilization, amniocentesis, termination of pregnancy... 66% 61 Changing gender relations, roles and relationships of women and men inside and outside the home... 59% 27 Other gender violence such as feminicide, trafficking of girls and women, FGM... 54% 253 Family law, family codes, property law, inheritance law and rights... 53% 57 Chi Id abuse, sexua I violence against chi Idren, neg lect 48% 361 Family relations, inter-generational conflict, single parents... 47% 95 HIV and AIDS, incidence, policy, treatment, people affected... 43% 65 Sexual harassment against women, rape, sexual assault, #MeToo #TimesUp 41% 457 Celebrity news, births, marriages, deaths, obituaries, famous people, royalty... 38% 582 Human rights, women's rights, children's rights, gay & lesbian rights, rights of minorities.. 38% 510 Education,childcare, nursery, university, literacy 37% 1486 Poverty, housing, social welfare, aid to those in need... 37% 583 Arts, entertainment, leisure, cinema, theatre, books, dance... 36% 992 Women politicians, women electoral candidates... 35% 1114 Other stories on social or legal issues (specify the topic in 'Comments'section of coding sheet) 32% 557 Consumer issues, consumer protection, regulation, prices, consumer fraud... 31% 352 Other stories on science or health (specify the topic in'Comments'section of coding sheet) 31% 200 Medicine, health, hygiene, safety, disability, medical research, funding (not EBOLA or HIV-Al DS)... 31% 3139 Climate change, global warming 30% 122 Employment 30% 548 Other epidemics, viruses, contagions, Influenza, BSE, SARS. NOT C0VID-19 (For stories related to Covid-19 30% 537 choose the closest relevant sub-topic) Religion, culture, tradition, cultural controversies, teachings, celebrations, practices... 29% 382 Other stories on celebrities, arts, media (specify the topic in'Comments'section of coding sheet) 29% 105 Environment, pollution, tourism... 29% 656 GMMP2020 30 Who Makes the News? Topic % Women N Migration, refugees, asylum seekers, ethnic conflict, integration, racism, xenophobia... 28% 321 Informal work, street vending,... 28% 92 Violent crime, murder, abduction, kidnapping, assault, drug-related violence... 28% 1823 Other stones on crime and violence (specify the topic in 'Comments' section of coding sheet) 27% 353 Disaster, accident, famine, flood, plane crash, etc 27% 1220 Media, including new media (computers, internet), portrayal of women and/or men 26% 198 Use only as a last resort and explain 26% 1353 Sustainable Development Goals (SDGs), Post 2015 agenda, Agenda 2030 25% 68 Science, technology, research, funding, discoveries, developments... 25% 530 Global partnerships (international trade and finance systems,e.g. WTO, IMF, World Bank, debt)... 24% 217 Other stories on the economy (specify the topic in'Comments'section of coding sheet) 24% 328 Non-violent crime, bribery, theft, drug-dealing,... 24% 985 Legal system, judicial system, legislation (apart from family, property & inheritance law)... 23% 1215 Other stories on politics and government (specify the topic in Comments'section of coding sheet) 23% 742 Other development issues, sustainability, 22% 345 Economic crisis, state bailouts of companies, company takeovers and mergers... 22% 656 Fake news, mis-information, dis-information, mal-information... 21% 91 Transport, traffic, roads... 20% 578 Rural economy, agriculture, farming practices, agricultural policy, land rights... 20% 608 Corruption, (including political corruption/malpractice) 20% 1053 Other labour issues, strikes, trade unions, negotiations, other employment and unemployment 19% 365 Other domestic politics/government (local, regional, national), elections, speeches, the political process... 19% 6174 Economic policies, strategies, modules, indicators, stock markets, taxes,... 18% 1853 Riots, demonstrations, public disorder, etc. 18% 383 Peace, negotiations, treaties...(local, regional, national), 16% 638 Foreign/international politics, relations with other countries, negotiations, treaties, UN peacekeeping... 16% 2205 National defence, military spending, military training, military parades, internal security... 15% 625 EBOLA, treatment, response... 15% 34 Sports, events, players,facilities, training, policies,funding ... 15% 2017 War, civil war, terrorism, state-based violence 12% 843 * Raw N and Weighted percentage shown.AlowN indicates fewer people in the stories published or broadcast on the respective topic on the global monitoring day. The percentages showthe proportion of the people in the stories who are women. GMMP2020 31 Who Makes the News? Story scope The likelihood for women to make the news diminishes as the story's scope broadens from the local to the global (Table 24). They are almost three in 10 subjects and sources in local news but only just over two in 10 in foreign/international stories in which their level of voice and visibility has trended downwards since 2010. Women's presence has risen fastest in national news, slowest in international coverage, and at matching paces in the case of local and regional stories. Table 24. Female news subjects in Local, national, regional and international stories in newspapers, television and radio. 1995-2020. 1995 2000 2005 2010 2015 2020 Local 22% 23% 27% 26% 27% 29% National 14% 17% 19% 23% 23% 25% Nationaland other' 17% 15% 18% 20% n/a n/a Sub-regional, Regional n/a n/a n/a n/a 26% 24% Foreign, International 17% 14% 20% 26% 24% 21% Overall 17% 18% 21% 24% 24% 25% 'Subsumed into the sub-regional / regionaľgrouping since 2015 Functions in the news As subjects or the people whom the stories are about, the proportion of women has more or less stagnated in traditional mediums since 2005 when this indicator was introduced into the monitoring, from 23% fifteen years ago to 24% presently. (Table 25) They are more likely to be subjects in news published on digital platforms, particularly on news websites. It could be argued that the capacities in which people speak or have voice in the news symbolize the value placed on their opinion. Gender disparities in these roles or functions suggest the worth accorded to people's voices on the basis of gender identity. Women's participation as experts is higher than five years ago, rising from 19% in 2015 to 24% and seven points in 15 years. In recent years numerous initiatives to source women for expert opinion have sprouted around the globe, with the compilation of various directories of women experts8 for use by journalists. Media organisations are visibly making efforts to diversify their experts' pools, pressured as well by civil society through, for example, the anti-'Manels' (male only panels) campaigns on social media. 8 See for example Les Expertes, https://expertes.fr/le-projet/ international directory of women gender experts; Gage directory of women and gender minorities in science, technology, engineering, mathematics and medicine https://gage.500womenscientists.org; SheSource by the Women's Media Centre, USA. CASE STUDY Macau SAR PRC Japan Airlines embraces gender neutral greetings Published in Print, in The Macau Post Daily la p J i-.t, i r 11 nm emh ro«?o gŕl ider n e iílf a] greeting! Summary The story is about the first airline in Japan to scrap the expression "ladies and gentleman" and adopt gender-neutral greetings, as a commitment to tackle gender-based discrimination. It has also pays attention to context on how LGBTO population in Japan has campaigned for greater recognition from the Government. Analysis The focus on an issue such as gender-neutral greetings raises attention to how certain expressions can perpetuate the exclusion of people - which in this case goes beyond conveying how it affects men and women, representing instead a commitment not to discriminate based on gender at all. GMMP2020 32 Who Makes the News? Table 25. News subjects and sources. % Women, by function, by medium. 2005-2020. 2005 2010 2015 2020 Function in news story p/R/r p/R/r p/R/r p/R/r News websites News media Tweets Subject: the story is about this person, or about something the person has done, said etc. 23% 23% 26% 24% 28% 26% Spokesperson: the person represents, or speaks on behalf of another person, a group or an organization 14% 19% 20% 22% 25% 19% Expert or commentator: the person provides additional information, opinion or comment based on specialist knowledge or expertise 17% 20% 19% 24% 25% 24% Personal experience: the person provides opinion or comment, based on individual personal experience; the opinion is not necessarily meant to reflect the views of a wider group 31% 36% 38% 42% 41% 41% Eye witness: the person gives testimony or comment, based on direct observation (e.g. being present at an event) 30% 29% 30% 30% 30% 36% Popular opinion: the person's opinion is assumed to reflect that of the ordinary citizen' (e.g., in a street interview, vox populi etc.); it is implied that the person's point of view is shared by a wider group of people. 34% 44% 37% 38% 39% 21% *PRT= Newspapers, radio and television news Trans and gender minorities in the news 1000 RADIO NEWSCASTS 2 in 1000 in radio newscasts 1000 TV & NEWS WEBSITE POSTS ■ 1 in 1000 on television and news websites 10000 PRINT NEWSPAPER ART!CUES a ;:: 7 in 10,000 in print newspapers 10000 MEWS MEDIA TWEETS ft ;:: 5 in 10,000 in news media tweets Transgender and other gender minorities are 0.2% on radio, 0.1% of those on television and news websites, .07% in print, .05% in news media tweets. GMMP2020 33 Who Makes the News? On news content related to Covid-19 Media research on disaster- and pandemic-reporting provides pointers on frameworks to understand content on the global health catastrophe that is Covid-19. Media are crucial for spreading awareness on crises and promoting as well as directing public and state response. Assessment of media content during such times zooms in on two issues: media framing, and treatment of marginalized and vulnerable groups. Ribeiro et al's (2018) study of 186 articles published between December 2015 and May 2016 at the height of the Zika epidemic revealed a neglect in media of the social-economic aspects of the disease. The analysis found "a dominant 'war' frame supported by two sub-frames: one focused on eradicating the mosquitos and another on controlling microcephaly, placing the burden of prevention on women. This frame gave prominence and legitimacy to certain representations of disease management during the crisis, masking social and gender inequalities". The researchers point out the print media's strong influence on debates taking place on digital media as the issues are reproduced in online press and social media platforms. Indeed, various organisations noted an intensification of social and gender inequalities, including violence against women, during Covid-19. Critical scholarship on disaster reporting points to the effects on women, marginalized groups and minorities. Hines' (2007) analysis of Indian media coverage of the 2004 Tsunami concluded that overlooking and ignoring gender concerns led to greater marginalization and impact on women. McKinnon, Gorman-Murray, & Dominey-How-es (2017) found a heteronormative bias and reporting that did little to improve knowledge of LGBTI vulnerabilities in disasters in Australia and New Zealand. Tyree & Hill's (2016) metanalysis of more than 30 studies with a media focus on coverage about or including African Americans impacted by Hurricane Katrina reached three conclusions: perpetuation of racist stereotypes, a mirroring of negative international media coverage of disasters, and media's harmful role in the ensuing State and public response. Disaster coverage tends to be gender-blind. As Seager (2006) underscores, the gendered impacts of Hurricane Katrina were out of the media picture in (U.S) local coverage just as in reporting on the Kobe 1995 earthquake and the Southeast Asian Tsunami in 2004. Women's presence as subjects, sources and journalists in stories related to Covid-19 maybe higher than in stories that are not about the pandemic but the quality of content from a gender perspective is worse. (Table 26) Stories about or regarding a dimension of the coronavirus focus on women four points less, they are less likely to raise gender equality or inequality issues, or to clearly challenge gender stereotypes. Table 26. Comparing Covid-19-related and non-Covid stories. 2020. Stories related to Covid-19 Non-Covid Traditional news, subjects & sources. %W 28% 25% Digital news, subjects & sources. %W 28% 27% Reporters in print and online news 44% 37% TV Stories in which women are central 2% 6% TV Stories that raise issues of gender (in)eguality 2% 4% TV Stories that raise clearly challenge gender stereotypes 2% 3% Apart from the small sample "gender & related" topic, women are more likely to appear in pandemic stories related to social/legal issues particularly on television where they are 38% of subjects and sources, as well as in news media tweets. (Table 27) The possibilities that a story will be about a woman or will carry a woman's voice are slimmest in Covid-19 stories that are also about politics and government. GMMP2020 34 Who Makes the News? Table 27. Subjects and sources in Covid-19 news. % Women, by major topic, by medium. 2020. News News media Print Radio Television websites tweets Overall Celebrity, Arts and Media, Sports 29% 24% 21% 25% 17% 25% Crime and Violence 29% 30% 25% 25% 24% 26% Economy 26% 26% 31% 25% 28% 27% Gender & Related 63% 75% 59% 70% 50% 66% Politics and Government 22% 24% 23% 25% 17% 23% Science and Health 25% 29% 32% 29% 33% 29% Social and Legal 32% 36% 38% 34% 38% 35% N 4230 1681 3334 3027 538 3102 On television, the medium whose importance has sky-rocketed during Covid-19 as seen in unprecedented high ratings, women as interviewees are between 5 to 8 points higher in pandemic than non-pandemic news. (Table 28) From expert opinion providers to those speaking based on personal experience, women's presence as sources is greater in pandemic stories and crosses over the gender parity line on radio; in this medium, women are 55% of popular opinion givers and 52% of those providing testimony based on personal experience. (Table 29) Table 28. Comparing Covid-19-related and non-Covid stories on Television, Functions of subjects and sources, %Women.2020. Covid-19 news Non-Covid Subject 23% 24% Spokesperson 26% 21% Expert or commentator 29% 21% Personal Experience 45% 37% Eye Witness 38% 33% Popular Opinion 44% 37% Table 29. Subjects and sources in Covid-19 news. % Women, by function, by medium. 2020. News websites Newspapers Radio Television News media Tweets Subject 27% 26% 23% 23% 25% Spokesperson 28% 23% 23% 26% 26% Expert or commentator 23% 24% 31% 29% 35% Personal Experience 41% 46% 52% 45% Eye Witness 48% 27% 38% Popular Opinion 27% 41% 55% 44% *Small sample of people coded under the respective functions and mediums GMMP2020 35 Who Makes the News? Table 30. Functions of female news subjects, by region. 2020. Africa Asia Caribbean Europe Latin America Middle East North America Pacific Subject 22% 21% 25% 25% 25% 19% 29% 29% Spokesperson 18% 15% 25% 28% 23% 12% 28% 27% Expert or commentator 21% 19% 29% 24% 25% 17% 38% 33% Personal Experience 46% 32% 53% 44% 41% 28% 46% 44% Eye Witness 19% 25% 48% 36% 35% 3% 23% 50% Popular Opinion 30% 29% 58% 45% 41% 22% 25% 33% Occupations Women are almost seven in 10 of news subjects and sources portrayed as homemakers similar to the 2015 results and their ranks among the unemployed, following the news picture, have increased by about eight points in the past five to 20 years. The news media's depiction of women as part - or not - of the economically active population, seems to follow early narratives in official statistics that presented women as being unengaged in productive life relative to men (cf. Waring, 1988). While understanding and acknowledgement of women's contributions have grown, the same would not be said of the news media. Taking for example the gender gap in persons appearing as health professionals in stories related to Covid-19 across all mediums, there is a clear wide difference between the physical and the news worlds. (Chart 1). Women are 27% of the health specialists appearing in coronavirus stories compared to 46% (global average) in the physical world following statistics from the WHO Global Health Workforce, and the news picture is only weakly correlated to reality (r2-0469). Table 31. Women's share of occupations according to the news. 2000-2020. 2000 2005 2010 2015 2020 Sex worker n/a n/a 39% 50% 95% Homemaker, parent (male or female)) only if no other occupation is given 81% 75% 72% 67% 68% Child, young person no other occupation given n/a 44% 46% 34% 54% Health worker, social worker, childcare worker n/a n/a n/a 47% 47% Student pupil, schoolchild 46% 51% 54% 59% J 46% Not stated n/a n/a n/a 45% 43% Villager or resident no other occupation given n/a 39% 39% 39% 42% Office or service worker, non-management worker 35% 40% 45% 35% 42% Unemployed no other occupation given 33% 19% 35% 34% 42% Celebrity, artist actor, writer, singer,TV personality 45% 42% 41% 33% 41% Retired person, pensioner no other occupation given 35% 33% 35% 35% 40% Other 44% 42% 41% 38% 39% Activist or worker in civil society org., NG0, trade union 24% 23% 34% 33% 35% Doctor, dentist, health specialist n/a n/a n/a 30% 29% Academic expert, lecturer, teacher n/a n/a n/a 23% 29% Media professional, journalist,film-maker,etc. n/a 36% 29% 21% 29% Lawyer, judge, magistrate, legal advocate, etc. n/a 18% 17% 22% 25% GMMP2020 36 Who Makes the News? 2000 2005 2010 2015 2020 Agriculture, mining, fishing, forestry 15% 13% 13% 14% 24% Government employee, public servant, etc. 12% 17% 17% 20% 22% Tradesperson, artisan, labourer, truck driver, etc. 15% 23% 22% 21% 21% Business person, exec, manager, stock broker... n/a 12% 14% 16% 20% Science/technology professional, engineer, etc. 12% 10% 10% 10% 20% Government, politician, minister, spokesperson... 10% 12% 17% 18% in Royalty, monarch, deposed monarch, etc. n/a 33% 31% 22% 16% Sportsperson, athlete, player, coach, referee 9% 16% 11% 7% 14% Police, military, para-military, militia, fire officer 4% 5% 7% 8% 12% Criminal, suspect no other occupation given 7% 9% 8% 12% 11% Religious figure, priest, monk, rabbi, mullah, nun 9% 21% 13% 5% 7% Chart 1. GMMP 2020: Comparing health specialists in Covid-19-related news, % women, and doctors in the physical world, % women Botswana iNorway Kyrgysstan So*h Herzegovina Ecuador • • Romania • ess- Turkey» Chi,,. ™ # Costa Rica France« Portugal» ^>olmü -— """" fmM Uruguay» Soain- • »„ Cyjjjus KlnEj^°n l3# Switzerland Tunisia, Israel •Pem Nigeria N*al Guinea Senegal 10 15 20 25 30 35 40 45 50 55 50 55 70 75 :80 85 90 95 Medical doctors % women Data sources: WHO Global Health Workforce Statistics GMMP 2020 GMMP 2020 37 Who Makes the News? Table 32. Top 5 occupations for women and men according to the news. 2020 WOMEN Politician/ member of parliament,... ...(24% of women in the news) Government employee, public servant, spokesperson, etc. (14%) Activist or worker in civil society org., NGO, trade union (10%) Celebrity, artist, actor, writer, singer, TV personality (6%) Academic expert, lecturer, teacher (5% MEN Politician/ member of parliament,... ...(35% of men in the news) m Government employee, public servant, spokesperson, etc. (13%) Business person, exec, manager,stock broker...(5%) Sportsperson, athlete, player, coach, referee (5%) Police, military, para-military, militia, fire officer (4%) Table 33. Functions of news subjects, by sex, by occupation. 2020. Expert or Personal Subject Spokesperson commentator Experience Eyewitness Popular Opinion Female Male Female Male Female Male Female Male Female Male Female Male Not stated 14% 6% 3% 1% 2% 1% 27% 20% 31% 26% 45% 36% Royalty, monarch, deposed monarch,etc. 1% 2% 0% 1% 0% 0% 0% 0% 0% 0% 0% 0% Politician/ member of parliament,... 29% 43% 33% 39% 18% 20% 4% 10% 7% 17% 6% 16% Government employee, public servant, spokesperson, etc. 7% 8% 23% 23% 13% 13% 2% 4% 4% 6% 1% 3% Police, military, para-military, militia, fire officer 1% 3% 3% 6% 3% 5% 1% 2% 1% 5% 0% 1% Academic expert, lecturer, teacher 2% 1% 3% 2% 13% 14% 4% 2% 5% 3% 3% 2% Doctor, dentist, health specialist 1% 1% 3% 2% 13% 10% 2% 3% 1% 1% 0% 0% Health worker, social worker, childcare worker 2% 0% 2% 1% 4% 2% 3% 1% 1% 1% 1% 1% Science/technology professional, engineer, etc. 1% 0% 0% 0% 2% 3% 0% 1% 0% 0% 0% 0% Media professional, journalist,film-maker, etc. 2% 2% 2% 1% 5% 5% 2% 2% 1% 1% 1% 1% Lawyer, judge, magistrate, legal advocate, etc. 4% 3% 4% 4% 8% 8% 0% 1% 3% 2% 0% 1% Business person, exec, manager, stock broker... 3% 4% 6% 7% 5% 7% 3% 6% 3% 3% 0% 3% Office or service worker, non-management worker 1% 0% 1% 0% 0% 1% 3% 3% 2% 1% 2% 1% Tradesperson, artisan, labourer, truck driver, etc. 1% 1% 0% 1% 0% 0% 2% 7% 2% 5% 4% 5% Agriculture, mining, fishing, forestry 1% 1% 0% 0% 0% 0% 2% 4% 0% 2% 0% 2% Religious figure, priest, monk, rabbi, mullah, nun 0% 1% 0% 1% 0% 1% 0% 1% 1% 2% 0% 1% Activist or worker in civil society org., NGO, trade union 3% 1% 11% 6% 10% 5% 2% 3% 2% 2% 3% 3% Sex worker 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Celebrity, artist, actor, writer, singer, TV personality 9% 4% 1% 1% 2% 1% 3% 1% 2% 1% 1% 2% GMMP2020 38 Who Makes the News? Sportsperson, athlete, player, coach, referee 5% 8% 1% 2% 0% 2% 1% 5% 1% 3% 0% 1% Student, pupil, schoolchild 3% 1% 0% 0% 0% 0% 10% 6% 4% 3% 5% 3% Homemaker, parent (male or female)) only if no other occupation is given e.g. doctor/ mothercode 6 3% 0% 0% 0% 0% 0% 11% 3% 9% 2% 7% 2% Child, young person no other occupation given 3% 1% 0% 0% 0% 0% 3% 1% 4% 1% 0% 1% Villager or resident no other occupation given 1% 1% 0% 0% 0% 0% 10% 8% 11% 10% 18% 13% Retired person, pensioner no other occupation given 1% 0% 0% 0% 0% 0% 1% 1% 1% 1% 1% 1% Criminal, suspect no other occupation given 2% 5% 0% 0% 0% 0% 0% 1% 0% 1% 0% 1% Unemployed no other occupation given 0% 0% 0% 0% 0% 0% 2% 2% 0% 0% 1% 0% Other only as last resort & explain 1% 1% 1% 1% 1% 1% 3% 2% 3% 2% 1% 1% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% Objectification of women in the news In 2020, women are still seen, and their physical attributes described more than their voices are heard in the news. A person's age is not always relevant to the story, and images in journalism are included to grab a reader's attention. At the same time, the greater propensity to describe women more than men, and to include their images particularly in various stages of undress, betray a different underlying motive. "When women do show up in the news, it is often as eye candy, thus reinforcing women's value as sources of visual pleasure rather than residing in the content of their views" (Jia et al. 2016), women's images are used to titillate or excite an assumed male audience. In the GMMP 2020 sample, 37% of women compared to 33% of men appeared in multimedia clips in online news, and 27% of women in contrast to 24% of men were photographed in print news. Age was not coded for 67% of women and 74% of men in newspapers, replicating patterns in earlier waves of a higher likelihood for women to be described in terms of their age in print news. In newspapers, the age of the person is captured only if it is explicitly mentioned in the story. Women are also more likely to appear or be described by age in television images; 84% of women compared to 82% of men in the broadcasts were coded for age. On television, the person's age is coded if it is specifically cited in the story or the person is visible in an image accompanying the story. Figure 3. Age of subjects and sources in print news. Distribution by sex. 2020. ■ 6% 8% 20% 31% 21% 10% 4% ■I 1. 12- 13-18 19-34 35-49 50-64 65-84 85+ WOMEN 2% 3% 13% 23% 35% 21% 2% 12- 13-18 19-34 35-49 50-64 65-84 85+ MEN In print news, men who are 50 years and older are very likely to be in the news, 42% of all people in the news belong to this category. The largest age group for women in the news is 35-49 years, whereas men peak in visibility from 50 to 64 years of age. (Figure 3) GMMP 2020 39 Who Makes the News? Men retain media attention until they reach 79 years of age. After 80, women and men are more or less equally present, albeit with men dominating this age group (60% men) (Table 34). Over time in newspapers and on televi- sion, women above 50 have become more invisible. In 2020, age group 50-64 was only 20% women in newspapers and 19% on television. Table 34. Age of news subjects in newspapers, % Women. 2005-2020. 2005 2010 2015 2020 12 and under 21% 41% 41% 55% 13-18 58% 38% 53% 54% 19-34 44% 36% 45% 41% 35-49 42% 33% 45% 37% 50-64 37% 22% 23% 20% 65-79' 24% 43% 42% 17% 80 years or more „ •• „ 40% "Age group'65 and over'in the 2005-2015 GMMPs "New category added in 2020. In prior waves persons aged 80 years or more were coded under '65 years or more' GMMP 2020 confirms the ongoing gendered ageism in the news media that is especially visible in the older age groups. Only 3 percent of all women in the news are found in the age group 65-79, compared to 15 percent of the men. 2020 is also the first time that the GMMP investigated the representation of people 80 years and above in the news. On a global scale we are getting older and healthier, older people now have better life both in terms of biological health and in terms of life satisfaction. This development could have attracted attention in the news but it has not. 2020 was also the first year of the global Covid-19 pandemic, where old age was considered a common denominator for being at risk, but people in the oldest age group rarely got attention in the news; only 3 percent were above 80 years in the newspapers and in television news less than 1 percent were above 80 years of age. Women 80+ were even more invisible than the men in that age group. Victims and survivors Women and men are more likely to be described as victims instead of as survivors since this indicators were first measured in 2005. Portrayal as victims has fallen over time for women and risen for men to more or less equalize the proportion by sex of victims in the news. (Table 35) Portrayal as survivors has fallen dramatically for men by 6 points across 15 years (from 8% in 2015 to 2% in 2020) while for women, the proportion has returned to the 2005 level (4%) after a steady rise until 2015. Currently, most subjects and sources described as victims or survivors- women and men alike - are as a result of accidents, disaster, poverty and disease. The pattern changes for all other victim and survivor categories. The second most prevalent victim- and survivor-type for women is coded under domestic violence, psychological violence, physical assault, marital rape, murder and similar crimes; for men, it is as victims of other crime, robbery, assault, (non-domestic) murder and similar, and as survivors of war, terrorism, vigilantism and state-based violence. Not surprisingly, women are portrayed as victims or survivors of non-domestic sexual violence, sexual harassment, rape and trafficking much more than men are (Women victims=10%, women survivors^ 13%, compared to men victims=2%, men survivors=3%). In Latin America where the study also responded to the question whether the subject or source was identified as a perpetrator of crime, men were 8 out of 10 of those coded as perpetrators and 9 out of 10 in stories specific to gender-related news specific to sexual harassment against women, rape, sexual assault, fe-minicide, trafficking of girls and women and similar stories. i GMMP Monitoring Day: Suriname GMMP 2020 40 Who Makes the News? Table 35. Victims and survivors in the print, television and radio news, by sex. 2005-2020 Victim Women Men 2005 2010 2015 2020 2005 2010 2015 2020 Accident, natural disaster, poverty, disease, illness... 32% 31% 40% 23% 36% 32% 35% 30% Domestic violence, psychological violence, physical assault, marital rape, murder... 9% 15% 20% 21% 6% 7% 24% 11% Other victim 14% 19% 14% 16% 10% 14% 11% 16% Other crime, robbery, assault, murder... 20% 11% 10% 15% 22% 16% 11% 19% Non-domestic sexual violence or abuse, sexual harassment, rape, trafficking... 7% 7% 5% 10% 2% 2% 1% 2% Discrimination based on gender, race, ethnicity, age, religion, ability... 4% 9% 5% 7% 3% 8% 3% 6% War, terrorism, vigilantism, state-based violence... 12% 7% 4% 7% 21% 17% 13% 15% Violation based on religion, tradition, cultural belief, genital mutilation, bride-burning... 2% 1% 1% 1% 1% 4% 1% 2% TOTAL PORTRAYED AS VICTIMS 19% 18% 16% 14% 8% 8% 8% 15% Survivors Women Men 2005 2010 2015 2020 2005 2010 2015 2020 Accident, natural disaster, poverty, disease, illness... 42% 35% 36% 28% 52% 38% 24% 35% Domestic violence, psychological violence, physical assault, marital rape, murder... 6% 13% 27% 20% 4% 4% 39% 9% Other survivor 15% 13% 11% 15% 10% 13% 10% 13% Crime, robbery, assault, murder... 17% 10% 8% 10% 15% 12% 8% 16% Non-domestic sexual violence or abuse, sexual harassment, rape, trafficking ... 10% 11% 4% 13% 3% 1% 3% 3% Discrimination based on gender, race, ethnicity, age, religion ... » 7% 8% 7% » 9% 3% 3% War, terrorism, vigilantism, state-based violence ... 10% 10% 5% 6% 16% 18% 13% 19% Violation based on religion, tradition, cultural belief, genital mutilation, bride-burning... n/a 1% 0% 1% n/a 5% 1% 2% TOTAL PORTRAYED AS SURVIVORS 4% 6% 8% 4% 8% 3% 3% 2% GMMP2020 41 Who Makes the News? Gender equality in the news, democracy, and the Good Society Monika Djerf-Pierre Gender equality in the news media is first and foremost a human rights issue. As such, gender equality in the news is a crucial aspect of media quality that has an intrinsic value in and of itself, regardless of the 'effects' that can be linked to its presence or absence. Still, the systematic and persistent lack of gender equality in the news media across the globe is also consequential for other parts of political, economic, and social life. In their globally appraised book, The Spirit Level, authors Wilkinson & Pickett (2009) show that equal societies, in terms of income equality, are indeed better to live in for everyone. Equal societies display lower levels of crime and violence, fewer social problems, better health for all (longer life expectancy, lower levels of mental illness and drug use), as well as higher levels of social trust, happiness, and satisfaction with life. Thus, income equality is an important determinant of a Good Society. The question is if gender equality in the media also, in fact, makes it "better for everyone" and if gender equality in the news matters for societal development and the quality of other social, economic, and political institutions (Djerf-Pierre 2011). Recent comparative research provides systematic empirical evidence for that this really is the case. Looking at countries across the globe, Djerf-Pierre (2020) identifies a positive relationship between the level of gender equality in the news media and the level of democracy as well as the freedom of the press. There is also a strong association between gender equality in the news and women's general status in society, measured by composite indices such as the Global Gender Gap Index (published by World Economic Forum) and the Gender Inequality Index (UNDP). The graph in Chart 2 shows an example of the positive association between gender equality in the news and democracy by plotting country-level estimates of gender equality in the news media (using data from GMMP 2020) and the level of democracy (using data from V-dem 2020). The pattern displayed in the scatterplot as well as the slope of the fitted line show that countries with higher levels of media gender equality and also have higher levels of democracy. Still, the association is not very strong (correlation coefficient r=.298, p=.003) and many countries have much higher levels of democracy than are predicted by their GEM-Index score (for example, Japan, Israel, Ghana, Senegal) whereas other countries have much lower levels of democracy than expected from their GEM-Index score (for example, Nicaragua, Central African Republic, Cuba, Chad, and Russian Federation). Still, establishing an association says very little about the causal direction; if more equality in the news promotes democracy or if democracy is driving the development of gender equality. The relationship is most likely reciprocal as the news media simultaneously reflect and are shaped by the social world. Media content mirrors, and thus reproduces, gender inequalities, while at the same time sometimes challenging and transforming them. CASE STUDY Italy Rome, fetuses buried with the name of the mothers without their consent. The anger of a mother. Media: www.leggo.it (Internet) The article denounces the procedures adopted by Italian hospitals to bury aborted fetus following the Catholic rite, affixing a cross on top of the grave and adding the mother's name. All of this regardless of the mother's consent or actual religion (or lack thereof) and without her being notified. This happened to the woman speaking in the story: she is upset by this happening to her and her aborted fetus, and by the lack of respect that conservative institutions show for mothers who, for whatever reason, undergo an abortion. The article reports the story and cites some of the victim's words, framing the episode as a violation of privacy and personal beliefs, since the woman in question isn't even catholic. The article reports the woman's point of view in an objective way without letting out any judgment against her choice to have an abortion, but challenging widespread conservative opinions about gender roles. It sympathizes with the woman even if it does not cite specific sources nor goes into much detail about the reality of women being denied basic rights or being forced into the role of nurturing mothers. All in all, the article presents the woman as upset, angry, but determined to denounce the fact and, possibly, to solve the problem. In doing so, the article challenges stereotypes about women as nurturing mothers.s team completely, and included only male administrators and players as sources. In fact, Newshub paid more attention to the schedule for Australian cricket than the New Zealand women. Media accountability score: B GMMP 2020 42 Who Makes the News? Chart 2. Correlating Gender Equality in the News and Level of Democracy 1 Uruguay * Denmark ___________ Costa Rktf0™"* * ^iJU* Netherlands Frances* lreland * Switzerland • Ne Belgium • Sweden ► Finland * New Zealand Japan • Ghana Senegal Luxembourg • Cyprus ► Argen tlna 1 Italy * Australia United Kingdom ^ • Chile Iceland * Canada Taiwan * United States of Amerca » Trinidad and Tobago 0.9 0.8 0.7 0.6 •..Ecua'dor « * Nam bia • Georgia ...•■*"^ Colombia Malawi • GuaterrlalaMexico Bulgaria * Moldova wl jatemalaMexico y=0.0049x+0.7035 R2 =0.0885 Papua New Guinea ^__JJt-BOminican Republic * Tanzania Bosnia and Herzegovina Malaysia Lebanon ■ Morocco • Haiti Bangladesh » Palestine * South Sudan Serbia -«-Jwdan • * Bollvia Uganda • Togo • Zimbabwe * Congo (the Democratic Republic of the) Egypt • Central African Republic * Cameroon • Vietnam • RUS^a a • Cambodia Chad 0.5 0.4 0.3 0.2 0.1 -90 -80 -70 -60 -50 -40 -30 Gender EauaLitv in the News (GEM-lndex) Score -20 -10 Data sources: 1. Coppedge, Michael, et al 2021."V-Dem [Country-Year/Country-Date] Dataset Vll.l." Varieties of Democracy Project 2: The GEM-lndex is a unitary measure of the level of gender equality in news media content and it is constructed to be theoretically informed, easy to apply and rate, broadly applicable to all forms of news media, and unidimensional and reliable in statistical terms. The index includes six indicators from the GMMP and considers the overall presence of women and men in the news, as well as their visibility and voice in specific gender sensitive roles and topics. The GEM-lndex calculates the average gender gap in the news (percentage of women - percentage of men) for the following six indicators: (1) all news subjects or sources ('people in the news'), (2) reporters, (3) news subjects or sources in economy and business news, (4) news subjects or sources in news about politics and government, (5) spokespersons and (6) experts. The GEM-1 can vary between -100 (only men in the news) and + 100 (only women in the news). Zero (0) represents full gender equality and a 50/50 distribution of men and women for all six indicators (see Djerf-Pierre & Edstrom, 2020 for an extensive description of the construction of the index). The liberal democracy index is retrieved from the V-dem dataset (Coppedge et al. 2021) and it considers the level of electoral democracy combined with the presence of constitutionally protected civil liberties, strong rule of law,and independent judiciary. References: 1. Coppedge, Michael, John Gerring, Carl Henri k Knutsen, Staffan I. Lindbergjan Teorell, Nazifa Alizada, David Altman, et a I. 2021."V-Dem [Country-Year/Country-Date] Dataset Vll.l." Varieties of Democracy Project. https://doi.Org/https://doi.org/10.23696/vdemds21. 2. Djerf-Pierre, Monika. 2011."The Difference Engine." Feminist Media Studies 11 (1): 43-51. https://doi.org/10.1080/14680777.2011.537026. 3. 2020. "Explaining Gender Equality in News Content: Modernisation and a Gendered Media Field." In Comparing Gender and Media Equality across the Globe: A Cross-National Study of the Qualities, Causes, and Consequences of Gender Equality in and through the News Media, edited by Monika Djerf-Pierre and Maria Edstrom, 147-189. Gothenburg: Nordicom, University of Gothenburg, https://doi.org/10.48335/9789188855329-4. 4. Djerf-Pierre, Monika, and Maria Edstrom. 2020. "The GEM-lndex: Constructing a Unitary Measure of Gender Equality in the News." In Comparing Gender and Media Equality across the Globe: A Cross-National Study of the Qualities, Causes, and Consequences of Gender Equality in and through the News Media, edited by Monika Djerf-Pierre and Maria Edstrom, 59-98. Gothenburg: Nordicom, University of Gothenburg, https://doi.org/10.48335/9789188855329-2. 5. Wilkinson, Richard, and Kate Pickett. 2009. The Spirit Level. Why Equality Is Better for Everyone. London: Penguin Books. GMMP 2020 43 Who Makes the News? in Reporters and presenters : Nudging the glass ceiling upwards General patterns The GMMP documents the sex of news personnel to the extent that they are visible through bylines, heard and seen in broadcast and digital content. Following stagnation between 2005 and 2015, women's visibility as reporters and journalists has increased by three percentage points overall across print and broadcast news. Since 2000, their newspaper byline credits visibility in newscasts has increased by 9% (Table 36) and online, 42% of journalists named in news articles, seen or heard in multimedia clips are women. Looking at presenters and announcers, women's overall presence has improved from their 2000 position, but remain below where they were in 2005. GMMP 2020 44 Who Makes the News? Table 36. Reporters and presenters. 1995 - 2020 1995 2000 2005 2010 2015 2020 A 20 years Presenters in radio newscasts • 41% 49% 45% 41% 46% +5% Presenter in television newscasts • 56% 57% 52% 57% 55% -l°/o OVERALL 51% 49% 53% 49% 49% 51% +2% Reporters in newspaper stories 25% 26% 29% 33% 35% 37% +11% Reporters in radio newscasts » 28% 45% 37% 41% 37% +9% Reporters in television newscasts • 36% 42% 44% 38% 45% +9% OVERALL 28% 31% 37% 37% 37% 40% +9% 'Breakdown by respective mediums not available Despite a slight roll-back in Latin America since the 2015 monitoring, the results suggest that the largest leap forward in women's participation in the news as presenters and reporters has been made in Latin America (+14 points, Table 37). The Caribbean region follows with a 10-point increase to cross over the half-way mark, further, only in this region is women's visibility as reporters squarely at parity (Table 38). The Middle East is back to where it started two decades ago on the indicator of the gender gap in stories presented and reported (Table 37), at the same time the greatest disparity is in Africa; in Africa as well, just over 3 in 10 sto- ries are reported by women (Table 38), 10 points behind its closest contender - the Asia region. That women find it easier to find work as presenters and announcers, than reporters and journalists is a pattern of employment which has been documented for decades, arguably as a consequence of an increasing intimisation of journalism for which women are seen as particularly suitable. However, young, attractive women are often paired with older, indifferently attractive men, speaking the soft, human interest news while the more authoritative male voice gives viewers the important headlines (Ross et al. 2018) Table 37. Female presenters and reporters in print, radio and television news, by region. 2000-2020 2000 2005 2010 2015 2020 A 20years Africa 36% 41% 34% 42% 39% +3% Asia 42% 49% 44% 47% 48% +6% Caribbean 41% 41% 34% 45% 51% +10% Europe 40% 42% 41% 41% 47% +7% Latin America 28% 38% 38% 43% 42% +14% Middle East 47% 41% 46% 50% 47% North America 46% 48% 35% 38% 47% +1% Pacific 49% 50% 35% 49% 57% +8% *1995 data not comparable due to difference in regional groupings The news reporter gender gap is exactly the same in Asia, Europe, and Latin America despite variations in the pace of change on this indicator across two decades. Pacific media have progressed slower than the rest of the world but they are currently the second-best performers after their Caribbean counterparts and only two points below parity. GMMP2020 45 Who Makes the News? Table 38. Female reporters in print, radio and television news, by region. 2000-2020. 2000 2005 2010 2015 2020 A 20 years Africa 24% 28% 30% 35% 32% +8% Asia 31% 37% 37% 31% 41% +10% Caribbean 39% 41% 45% 44% 50% +11% Europe 34% 34% 35% 37% 41% +7% Latin America 27% 44% 43% 41% 41% +14% Middle East 34% 35% 34% 38% 46% +12% North America 36% 35% 38% 40% 43% +7% Pacific 43% 44% 38% 45% 48% +5% We saw earlier how the likelihood for women to make the news lessens as the story's scope broadens from the micro to the macro (Table 24). Women's presence as reporters of foreign/international stories has been on a general upward trend since 1995. In sub-regional/regional news women's role as reporters has seen a steady rise for a decade now after a 10-year impasse between 2000 and 2010. In local reporting women's participation as reporters is recouping a loss in 2015 to reach a level similar to 10 years ago. The findings suggest an inverse correlation between both indicators: over time, the gender gap in subjects and sources is increasing in transnational news and becoming narrower in local coverage. Inversely, the gender gap in reporters is decreasing, and more rapidly, as coverage expands into regional and international news. One explanation is that both women and men working at the local level, have more latitude to choose a more diverse range of sources whereas journalists writing about foreign and international events are more likely to go to the usual suspect who will be predominantly men, given that stories about politics dominate both the national and international news agendas and where most senior politicians are men. Table 39. Stories by female reporters in traditional mediums, by scope. 1995-2020. 1995 2000 2005 2010 2015 2020 Local 33% 34% 44% 40% 38% 40% National 24% 30% 34% 38% 38% 41% Nationaland other' 28% 33% 32% 32% n/a n/a Sub-regional/regional - - - - 37% 40% Foreign/International 28% 29% 36% 37% 35% 38% 'Subsumed into the 'sub-regional / regional'grouping since 2015 Story allocation by major topic A comparison between print and digital newspapers reveals that stories by women reporters are distributed more or less evenly across the major topics in online and offline sources (Figure 4) as those by men are skewed towards the politics & government beat. 62% of the web-published newspapers monitored do not have print version, meaning that the similarities seen across platforms is not completely attributable to re-publication of print papers in the digital space; story assignment to online-only journalists is both similar to patterns of practice in physical newsrooms, as well as intensification of gendered practice in the online space. Historically, political journalism has had the most severe reporter gender disparity but has now improved to the second-last position, surpassing crime/violence reporting by two points (Table 40). Scholars have found political reporting to be a hostile beat for women, particularly online (see Usher, Holcomb, & Littman, 2018) from the GMMP GMMP2020 46 Who Makes the News? 2020 findings, the gender gap in political news coverage is lags behind significantly with only two in 10 stories on wider on news websites that in newspapers. Nevertheless, politics and government reported by women. we are seeing a noticeable change (+3%) in the proportion of political news reported by women for the first time since 2005. In the Caribbean, Middle East, and Pacific regions, at least 50% of political news are reported by women. Africa Table 40. Stories by female reporters in traditional mediums, by major topics. 2000-2020 2000 2005 2010 2015 2020 A 20 yrs Politics and Government 26% 32% 33% 31% 35% +9% Economy 35% 43% 40% 39% 41% +6% Science and Health 46% 38% 44% 50% 49% +3% Social and Legal 39% 40% 43% 39% 44% +5% Crime and Violence 29% 33% 35% 33% 33% +4% Gender and related - - - - 42% CeLebrity, Arts and Media, Sports 27% 35% 38% 34% 40% +13% Figure 4. Reporters by major topic, by sex. Comparing newspapers and news websites. 2020 Women are reporting more social/legal stories now than five years ago, their stories concentrated in three sub-topics: Education, childcare, nursery, university, literacy (27% of the women journalists reporting on this topic, all mediums overall); Disaster, accident, famine, flood, plane crash, etc. (14%), and; Legal system, judicial system, legislation (12% of the women journalists). At the global average level, the gender gap in reporters remains narrowest in the science & health major topic. (Table 40) One half of science stories were reported by women five years ago after a steady improvement since 2010, interestingly, as this topic's salience on the news agenda rose dramatically (from < 10 points in previous years to 17% currently) due to the pandemic, a slight gap has re-appeared. Performance in the regions varies on this topic; in all regions apart from Africa, Europe, and Latin America, women reporters are equally or over-represented. GMMP2020 47 Who Makes the News? Interestingly, women reported only 16% of the "gender & related" stories covering gender-based violence stories in Asia, a region that contributed a comparatively significant volume of articles on the topic to the overall sample. Of the GMMP major topic groupings with the exception of the "gender & related" category, women reporters are most underrepresented in the crime & violence beats in Europe, Latin America, and the Middle East. In Africa and Asia, the gender gap is widest in political news coverage while in the Pacific, celebrity, arts, media and sports stories are least likely to be reported by women. CASE STUDY Pakistan A mother's extraordinary protest in Waziristan following the murder of her son 0 § s.Bltt! j if Summary Set in the town of Wana, once in the eye of the storm during the war with the Taliban, the story revolves around an elderly woman from a nearby village who is protesting to have her kidnapped son recovered from his abductors. The case has largely been ignored by government authorities. The narrative starts at a protest camp outside the Wana Press Club where the woman is pictured. It emerges that four days earlier her son was travelling the main Wana bazaar in a private vehicle but never reached his destination. The woman pleads with law enforcement to start a search for her missing son but instead, a few days later, she is handed his dead body. Analysis For a story to come out of Waziristan with near-perfect gender balance in how it was crafted and reported is nothing short of remarkable: Waziristan is among those places in Pakistan where the media has little access and, as such, reporting out of Waziristan is seldom and complex. It is one of those places that was torn apart by war against the Taliban and it still bears signs of old wounds. It is also one of those places where tribal societies still exist, and the tyranny of patriarchal rules is fairly severe. This story ought to be understood in that context. In terms of headlines, sources and perspectives, the story checks all the right boxes. The headline centres the mother and nothing else. While operating under strict gaze of the state, it does not take any positions on the morality of the situation. Or the absence of law. But when we dive into the story, it makes mention of how patriarchal Waziristan's society is, how women there are often alienated by rights organizations since they don't seem to pick up on women's plight in Waziristan. And quite poignantly, it centres the woman between culture, patriarchy, and a rights framework. While the narrative revolves around the woman with a grandson in tow, the story has multiple respondents whose direct quotes appear in the story. It is through these voices that it begins to emerge that kidnapping is a rising trend in Waziristan, but while traditional jirgas used to negotiate the recovery of the victim and punishment for the accused, they now seem either disinterested or impotent. As with many other phenomena in Pakistan, distressed women are a window into a larger social phenomenon that is taking place. The story has the perfect protagonist whose struggle tugs at the audience's hearts, irrespective of them being women or men. Every development in her life is a window to how society is structured in Waziristan: rising kidnappings but without any writ of the law for the ordinary citizen, for example. Another is the rising number of older generation women becoming heads of households in what were war-torn areas; the story gives plenty of insights to a broader social phenomenon. GMMP 2020 48 Who Makes the News? Table 41. Female reporters in print, television and radio stories, by major topic, by region. 2020. Africa Asia Caribbean Europe Latin America Middle East North America Pacific Islands Politics and Government 21% 34% 55% 37% 40% 50% 43% 52% Economy 34% 42% 41% 43% 43% 40% 30% 49% Science and Health 43% 50% 56% 48% 48% 50% 50% 63% Social and Legal 38% 45% 54% 44% 42% 53% 46% 55% Crime and Violence 28% 38% 41% 31% 34% 25% 31% 51% Gender & Related 65% 16% 33% 55% 38% - 100% 0% Celebrity, Arts and Media, Sports 42% 41% 61% 41% 34% 45% 50% 24% REGIONALAVERAGE 32% 41% 50% 41% 41% 46% 43% 48% Drilling down into the sub-topics which sit beneath the composited main topics, it becomes clear that even though women journalists are writing across a more diverse range of beats than in previous years, there is still a degree of horizontal segregation (see North, 2016) occurring within those beats, so that they are more likely to be writing on topics which have traditionally been viewed as of particular interest to women (eg feminism, gender equality, human interest, education, childcare, LGBTO and welfare) and less likely to write stories about so-called male topics such as sport or security. Do more women reporters result in greater gender diversity in sources? Journalists may not consciously consider gender an important criterion for source selection (cf. Lobo, Silveirinha, Torres da Silva, & Subtil, 2017) but the GMMP findings across time indicate that women reporters are more likely than men to turn to women. In 2015, the results suggested that the gender source selection gap was narrowing, but in the 2020 wave, the gap has more than doubled to reach 7 points, from only 3% five years ago. Currently, 31% of the people in traditional news covered by women reporters are female, in contrast to 24% of subjects and sources in stories by men reporters (Table 42). There is a consistent 5-7% point gap between women and men reporters on female source selection in all regions except for the Caribbean where men reporters are almost as likely as their women colleagues to select female sources. (Figure 5) The pattern is repeated on digital news platforms where there is a nine-point gap in gender source selection, with 34% of female sources in stories by women reporters compared to 25% in stories by men reporters. (Table 43) Table 42. Female news subjects, by sex of reporter. Print, television and radio stories, 2000-2020. 2000 2005 2010 2015 2020 A 20yrs 24% 25% 28% 29% 31% +7% 18% 20% 22% 26% 24% +6% Female reporters Male reporters Figure 5. Female news subjects by sex of reporter. Print, television and radio news. 2020 WOMEN REPORTERS MEN REPORTERS i 19 30 23 27 26 32 27 31 25 19 14)35 28 33 ; Europe Latin Americ; North America Pacific Islands GMMP 2020 49 Who Makes the News? Table 43. Female news subjects, by sex of reporter. News websites. 2015-2020. 2015 2020 A 5yrs Female reporters 30% 34% +4% Male reporters 21% 25% +4% Overall the rise in the proportion of stories by women, and the increased propensity to select girls and women as subjects and sources, are promising for gender equality as far as the numerical counts are concerned. At the same time, the news media are working in environments that are becoming increasingly hostile for women, given the evidence and revisions in projections on the length of time it will take to achieve gender parity in various development sectors. (World Economic Forum 2015)(World Economic Forum 2021) In the year of the 5th GMMP, the forecast estimated 118 years to close the gender gap across health, education, economic opportunity and politics (World Economic Forum, 2015); in the latest report, the length of time to achieve parity has increased to at least 135 years[l] (World Economic Forum, 2021). [1] The report notes that the increase is driven largely by a decline in the performance of large countries but also underscores the contribution of Covid-19 to raising new barriers and halting progress towards parity. Table 44. Top 10 news stories most likely to be reported by women. 2020 Table 45. Stories least likely to be reported by women...the bottom 10*. 2020 Rank Topic Women's movement, feminist activism, events, demonstrations, gender equa lity advocacy... Rank Topic ^ Sports, events, players, facilities, training, policies, funding. 2 Other epidemics, viruses, contagions, Influenza, BSE, SARS." Other stories on crime and violence 3 Informal work, street vending,... J Peace, negotiations, treaties...(local, regional, national) Other stories on science or health 4 War, civil war, terrorism, state-based violence 5 Arts, entertainment, leisure, cinema, theatre, books, dance. Violent crime, murder, abduction, kidnapping, assault, drug-related violence... Celebrity news, births, marriages, deaths, obituaries, famous people, royalty... 7 Education,childcare, nursery, university, literacy 8 9 Human rights, women's rights, children's rights, gay & lesbian rights, rights of minorities.. Media, including new media (computers, internet), portrayal of women and/or men 10 Poverty, housing, social welfare, aid to those in need. Riots, demonstrations, public disorder, etc. 7 8 9 National defence, military spending, military training, military parades, internal security... Other stories on celebrities, arts, media Other domestic politics/government (local, regional, national) elections, speeches, the political process... Foreign/international politics, relations with other countries, 10 negotiations, treaties, U N peacekeeping... Excludes topics that had less than 30 stories captured during the global monitoring day StoriesrelatedtoCovid-19categorizedhereonlyifnoothersub-topicor secondary theme is found in the story. 'Excludes topics that had less than 30 stories captured during the global monitoring day GMMP 2020 50 Who Makes the News? Table 46. Topics in the news - Detaii by medium for femaie reporter. 2020 Print Radio Television Internet Twitter Female Male Female Male Female Male Female Male Female Male Women politicians, women electoral candidates... 1% 1% 2% 3% 2% 1% 2% 3% 2% 2% Peace, negotiations, treaties...(local, regional, national), 1% 2% 2% 2% 1% 1% 0% 2% 1% 0% Other domestic politics/government (local, regional, national), elections, speeches, the political process... 12% 15% 10% 11% 9% 11% 11% 13% 10% 13% Global partnerships (international trade and finance systems, e.g. WTO, IMF, World Bank,debt)... 1% 1% 1% 0% 0% 0% 1% 1% 0% 1% Foreign/international politics, relations with other countries, negotiations, treaties, UN peacekeeping... 3% 5% 5% 4% 5% 6% 4% 4% 5% 6% National defence, military spending, military training, military parades, internal security... 1% 2% 2% 2% 1% 2% 1% 2% 1% 2% Other stories on politics and government (specify the topic in 'Comments'section of coding sheet) 2% 2% 2% 1% 2% 1% 2% 3% 1% 2% Economic policies, strategies, modules, indicators, stock markets, taxes,... 6% 7% 6% 6% 4% 3% 5% 6% 4% 4% Economic crisis, state bailouts of companies, company takeovers and mergers... 3% 2% 2% 3% 2% 1% 3% 2% 3% 3% Poverty, housing, social welfare, aid to those in need... 3% 1% 1% 2% 2% 1% 2% 1% 2% 1% Women's participation in economic processes (informal work, paid employment, unemployment, unpaid labour) 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Employment 2% 1% 1% 1% 2% 1% 1% 1% 2% 1% Informal work, street vending,... 1% 0% 0% 0% 0% 0% 0% 0% 0% 0% Other labour issues, strikes, trade unions, negotiations, other employment and unemployment 1% 1% 1% 1% 1% 1% 2% 1% 2% 1% Rural economy, agriculture, farming practices, agricultural policy, land rights... 2% 2% 2% 1% 1% 1% 1% 1% 1% 1% Consumer issues, consumer protection, regulation, prices, consumer fraud... 1% 1% 1% 1% 2% 1% 1% 1% 2% 1% Transport, traffic, roads... 1% 2% 2% 3% 2% 2% 1% 2% 2% 3% Other stories on the economy (specify the topic in 'Comments'section of coding sheet) 2% 2% 1% 1% 1% 1% 1% 1% 1% 1% Science, technology, research, funding, discoveries, developments... 2% 1% 2% 2% 2% 2% 2% 2% 2% 2% Medicine, health, hygiene, safety, disability, medical research, funding (not EBOLAor HIV-AIDS)... 10% 7% 12% 9% 11% 9% 11% 8% 12% 6% EBOLA, treatment, response... 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% HIV and AIDS, incidence, policy, treatment, people affected... 0% 0% 0% 0% 1% 0% 0% 0% 0% 0% Other epidemics, viruses, contagions, Influenza, BSE, SARS. NOT C0VID-19 (For stories related to Covid-19 choose the closest relevant sub-topic) 1% 1% 2% 1% 3% 1% 1% 1% 1% 0% Birth control, fertility, sterilization, amniocentesis, termination of pregnancy... 0% 0% 0% 0% 0% 0% 1% 0% 1% 0% Climate change, global warming 0% 0% 1% 0% 0% 0% 0% 1% 0% 0% Environment pollution, tourism... 3% 2% 2% 2% 2% 2% 2% 2% 2% 2% Other stories on science or health (specify the topic in'Comments'section of coding sheet) 1% 0% 1% 1% 1% 1% 1% 1% 1% 1% Sustainable Development Goals (SDGs), Post 2015 agenda,Agenda 2030 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Family relations, inter-generational conflict, single parents... 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% 51 Print Radio Television Internet Twitter Female Male Female Male Female Male Female Male Female Male Human rights,women's rights,children's rights, gay & lesbian rights, rights of minorities... 1% 1% 1% 1% 1% 1% 2% 2% 2% 1% Religion, culture, tradition, cultural controversies, teachings, celebrations, practices... 1% 1% 1% 1% 1% 1% 1% 1% 1% 2% Migration, refugees, asylum seekers, ethnic conflict, integration, racism, xenophobia... 1% 1% 1% 1% 1% 1% 1% 1% 0% 1% Other development issues, sustainability, 1% 1% 1% 1% 2% 1% 1% 1% 1% 1% Education, childcare, nursery, university, literacy 6% 4% 4% 3% 5% 2% 5% 4% 4% 3% Women's movement, feminist activism, events, demonstrations, gender eguality advocacy... 1% 0% 2% 1% 1% 0% 1% 0% 2% 1% Changing gender relations, roles and relationships of women and men inside and outside the home... 0% 0% 0% 0% 0% 0% 1% 0% 0% 0% Family law, family codes, property law, inheritance law and rights... 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Legal system, judicial system, legislation (apart from family, property & inheritance law)... 3% 3% 2% 2% 2% 2% 3% 2% 2% 3% Disaster, accident, famine, flood, plane crash, etc 3% 2% 2% 3% 3% 5% 3% 3% 3% 1% Riots, demonstrations, public disorder, etc. 1% 1% 1% 1% 1% 1% 1% 1% 3% 2% Other stories on social or legal issues (specify the topic in'Comments'section of coding sheet) 2% 1% 2% 1% 1% 2% 1% 1% 1% 1% Non-violent crime, bribery, theft, drug-dealing,... 2% 3% 2% 1% 3% 3% 3% 2% 2% 2% Corruption, (including political corruption/malpractice) 1% 3% 2% 1% 2% 2% 2% 2% 1% 3% Violent crime, murder, abduction, kidnapping, assault, drug-related violence... 3% 5% 1% 4% 4% 4% 4% 5% 3% 4% Child abuse, sexual violence against children, neglect 1% 1% 1% 0% 1% 1% 1% 1% 1% 0% War, civil war, terrorism, state-based violence 1% 1% 1% 4% 2% 3% 1% 1% 2% 3% Other stories on crime and violence (specify the topic in Comments'section of coding sheet) 0% 1% 0% 1% 1% 2% 1% 1% 1% 0% Sexual harassment against women, rape, sexual assault, #MeToo #TimesUp 1% 0% 1% 0% 1% 1% 1% 1% 1% 1% Other gender violence such as feminicide, trafficking of girls and women, FGM... 0% 0% 1% 1% 0% 1% 1% 1% 2% 2% Ineguality between women and men such as income ineguality/gender pay gap, 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Celebrity news, births, marriages, deaths, obituaries, famous people,royalty... 3% 1% 1% 0% 1% 1% 3% 2% 3% 2% Arts, entertainment, leisure, cinema, theatre, books, dance... 4% 2% 4% 1% 3% 2% 2% 2% 4% 3% Media, including new media (computers, internet), portrayal of women and/or men 1% 0% 0% 1% 1% 0% 1% 1% 1% 1% Fake news, mis-information,dis-information, mal-in-formation... 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Beauty contests, models, fashion, beauty aids, cosmetic surgery... 0% 0% 0% 0% 0% 0% 0% 0% 0% 0% Sports, events, players, facilities, training, policies, funding... 1% 4% 3% 1% 3% 1% 2% 4% 3% 1% Other stories on celebrities, arts, media (specify the topic in 'Comments' section of coding sheet) 0% 0% 0% 0% 0% 1% 0% 0% 0% 0% Use only as a last resort and explain 2% 3% 6% 5% 3% 4% 1% 2% 2% 1% 100% 100% 100% 100% 100% 100% 100% 100% 100% 100% 52 v News quality from a gender perspective 1) Respect for the facts and for the right of the public to truth is the first duty of the journalist. 9) Journalists shall ensure that the dissemination of information or opinion does not contribute to hatred or prejudice and shall do their utmost to avoid facilitating the spread of discrimination on grounds such as geographical, social or ethnic origin, race, gender, sexual orientation, language, religion, disability, political and other opinions. Global Charter of Ethics for Journalists (2019)[1] Media ethics bind professional journalists to exercise accountability to audiences, to respect the public's right to fair and balanced portrayal enshrined in international law and industry standards and codes. The Global Charter of Ethics for Journalists underscores the duty of "journalists worthy of the name" to faithfully observe the principles agreed in the industry and reiterated across media in-house policies worldwide. Evidence from numerous media content analysis reveals however that the duty is flouted with impunity when the subject of the story is a woman and worse when she shares a minority or marginalized identity (cf. Galy-Badenas & Gray, 2020). In the recent past there have been innumerable initiatives to increase gender diversity in newsrooms and sourcing, with some positive results on these indicators. Sadly, the quality of news journalism from a gender perspective has not improved, if the numbers tell us anything, it is that stagnation and decline are consistent across the quality measures and common across regions and major topics at the global level. On gender stereotypes News stories are as (un)likely to clearly challenge gender stereotypes today as they were 15 years ago. (Table 47) Change across the one and a half decades hovers between -1 and +1 point within the major topics except for celebrity & sports stories that are likely to challenge gender stereotypes two points more today than in 2005. News disseminated through news websites and Twitter fare just or almost as poorly as stories in legacy media. The poor performance is replicated across regions (Table 48) with some variations across major topics. Looking at social/legal news, the topic most likely to challenge gender stereotypes - except for the gender-specific "gender & related" category - Pacific news media are the exemplary performers followed by the Middle East, Latin and North America regions. Interestingly, science & health stories are least likely to clearly challenge gender stereotypes, at a time when this topic has gained unprecedented prominence on the news agenda due to Covid-19. In the Caribbean, Europe, the Middle East and Latin America, only 1% of science/health news clearly challenge gender stereotypes. Between seven to nine out of 10 stories on sexual harassment, rape, other forms of gender violence and specific gender inequality issues reinforce or do nothing to challenge gender stereotypes, with implications for the normalization and continuance of the very injustices that are the focus of the stories. Across 15 years, news media in the Pacific region have made the greatest improvement as North American media have moved two points behind the 2005 performance. (Table 49) At the worldwide average level the status quo is observed; globally, only three percent of stories clearly challenge gender stereotypes, the same proportion found in 2005. GMMP2020 53 Who Makes the News? Table 47. Stories that clearly challenge gender stereotypes, by major topic. 2005-2020. 2005 2010 2015 2020 A15 yrs Topic Print, radio, television Print, radio, television Print, radio, television Print, radio, television News websites News tweets Print, radio, television Politics and Government 3% 5% 3% 2% 4% 2% -l°/o Economy 1% 4% 3% 2% 2% 2% +l°/o Science and Health 1% 5% 5% 1% 3% 1% 0 Social and Legal 6% 8% 4% 5% 5% 5% -l°/o Crime and Violence 2% 5% 4% 3% 3% 2% +l°/o Gender & Related 19% 15% 15% n/a CeLebrity, Arts and Media, Sports 2% 6% 3% 4% 7% 5% +2% Other 5% 2% 1% 5% 7% 3% 0 OVERALL 3% 6% 4% 3% 4% 3% 0 Table 48. Stories that clearly challenge gender stereotypes, by region, by major topic. 2020 Latin Africa Asia Caribbean Europe America Middle East North America Pacific OVERALL Politics and Government 3% 2% 3% 2% 1% 6% 2% 2% 2% Economy 2% 1% 2% 2% 2% 1% 0% 3% 2% Science and Health 2% 2% 1% 1% 1% 1% 3% 3% l°/o Social and Legal 5% 3% 3% 3% 7% 8% 7% 9% 5% Crime and Violence 5% 3% 0% 1% 4% 1% 0% 7% 3% Gender & Related 22% 21% 9% 30% 14% • • • 19% CeLebrity, Arts and Media, Sports 1% 2% 3% 4% 5% 7% 4% 5% 4% OVERALL 3% 3% 2% 2% 4% 4% 3% 5% 3% *Too few stories on the topic carried in the major news of the day on the global monitoring day Table 49. Stories that dearly challenge gender stereotypes, by region. 2005-2020. Region 2005 2010 2015 2020 A 15yrs Africa 3% 5% 5% 3% 0 Asia 2% 5% 3% 3% +1% Caribbean 3% 5% 8% 2% -1% Europe 2% 4% 3% 2% 0 Latin America 3% 13% 5% 4% +1% Middle East 3% 4% 2% 4% +1% North America 5% 9% 9% 3% -2% Pacific 1% 2% 1% 5% +4% GLOBAL AVERAGE 3% 6% 4% 3% 0 GMMP2020 54 Who Makes the News? The GMMP builds the data on the extent to which news journalists are likely to frame stories from a rights perspective by making reference to gender equality, women's and general human rights policy frameworks relevant to the topic. A smaller proportion of stories today make reference to relevant rights instruments, the decline being in stories on politics (-2% points), on science/health (-6%), on crime/violence (-4%) and celebrity/sports news (-3%). (Table 50). The past five years have seen a meteoric improvement in Pacific news content (+8% points) and fall in news media performance in the Caribbean (-7%) and Africa (-6%) on this indicator. (Table 51) African and North American news media perform best (Table 52), and North American social/legal news particularly with almost one third of stories bearing the rights angle. In five out of the seven regions, only 1-2% of celebrity & sports stories make reference to gender equality and/or women/human rights. Rights-centred journalistic practice Table 50. Reference to gender equality/human rights/policy, by major topic. 2015-2020. Major Topic 2015 2020 Politics and Government 8% 6% Economy 7% 8% Science and Health 9% 3% Social and Legal 12% 14% Crime and Violence 10% 6% Gender & Related n/a 53% CeLebrity, Arts and Media, Sports 5% 2% Other 5% 7% OVERALL 9% 7% Table 51. Reference to gender equality, women's rights and/or human rights policy, by region. 2010-2020. Region 2010 2015 2020 Africa 13% 20% 14% Asia 8% 8% 7% Caribbean 9% 19% 12% Europe 9% 5% 4% Latin America 5% 7% 7% Middle East 22% 6% 5% North America 21% 17% 14% Pacific 2% 1% 9% GLOBALAVERAGE 10% 9% 7% 55 Table 52. Reference to gender equality/human rights/policy, by major topic by region. 2020. North Africa Asia Caribbean Europe Latin America Middle East America Pacific Islands Politics and Government 12% 6% 12% 4% 5% 3% 14% 9% Economy 16% 9% 9% 4% 7% 4% 17% 6% Science and Health 11% 3% 6% 2% 3% 1% 2% 5% Social and Legal 20% 13% 17% 9% 13% 16% 32% 16% Crime and Violence 13% 5% 17% 3% 6% 6% 13% 12% Celebrity, Arts and Media, Sports 4% 1% 9% 2% 1% 1% 2% 4% OVERALL 14% 7% 12% 4% 7% 5% 14% 9% For two decades now, the GMMP has monitored the extent to which women make the news in significant ways as the main protagonists in the story. The 2020 edition reveals that women are less likely to feature centrally in the story now than 20 years ago; on this indicator, only six percent of stories have women as a central focus compared to 10% at the start of the millennium. Of the four GMMP gender news quality indicators - the other three being; on the likelihood to clearly challenge gender stereotypes, to make reference to gender equality / rights policy, and to raise issues of gender equality or inequality - it is performance on the "women's centrality in the news" measure that has declined most sharply across time. The results imply that women are marginal in stories at the core of the news agenda today more than ever. In two decades, they have lost centrality most in social & legal news (-7 points) and science/health (-7%) stories. Less than 1% of stories about environment, consumer issues and labour/ employment were found to focus on women (Table 56) Women's centrality in the news Table 53. Women's centrality in the news, by major topic. 2000-2020. 2000 2005 2010 2015 2020 A 20 yrs Print, radio, television Print, radio, television Print, radio, television Print, radio, television Print, radio, television News websites Print, radio, television Politics and Government 7% 8% 13% 7% 7% 12% 0°/o Economy 4% 3% 4% 5% 4% 4% 0°/o Science and Health 11% 6% 11% 14% 4% 8% -7% Social and Legal 19% 17% 17% 8% 12% 14% -7% Crime and Violence 10% 16% 16% 17% 14% 22% +4% CeLebrity, Arts and Media, Sports 16% 17% 16% 14% 13% 24% -3% OVERALL 10% 10% 13% 10% 9% 14% -1% * Correction: A data capture error resulted in under-counting the 'yes' responses for the indicator "% of stories in which women are central" in the earlier published report. This version shows the corrected finding. GMMP 2020 56 Who Makes the News? Table 54. Top 10* topics in which women are most Likely to be central. 2020 Rank Topic Women's movement, feminist activism, events, demonstrations, gender equality advocacy... Other gender violence such as feminicide, trafficking of girls and women, FGM... Birth control, fertility, sterilization, amniocentesis, termination of pregnancy... Women's participation in economic processes (informal work, paid employment, unemployment, unpaid labour) Sexual harassment against women, rape, sexual assault, #MeToo #TimesUp Beauty contests, models, fashion, beauty aids, cosmetic surgery... Chi Id abuse, sexua I violence against chi Idren, neg lect Women politicians, women electoral candidates... Family relations, inter-generational conflict, single parents... 10 Celebrity news, births, marriages, deaths, obituaries, famous people, royalty... 'Excludes topics with small samples (<30 stories coded) Table 55. Women's centrality...the bottom 10 stories. 2020 Rank Topic HIV and AIDS, incidence, policy, treatment, people affected... Environment, pollution, tourism... 3 Consumer issues, consumer protection, regulation, prices, consumer fraud... Transport, traffic, roads... Other labour issues, strikes, trade unions, negotiations, other employment and unemployment Economic crisis, state bailouts of companies, company takeovers and mergers... Economic policies, strategies, modules, indicators, stock markets, taxes,... Other stories on the economy Rural economy, agriculture, farming practices, agricultural policy, land rights... Other development issues, sustainability, GMMP2020 57 Who Makes the News? Table 56. Stories with women as a central focus, percentage by topic -detail. 2020. Topic % stories n Women's movement, feminist activism, events, demonstrations, gender equality advocacy... 83% 114 Inequality between women and men such as income inequality/gender pay gap, 82% 17 Other gender violence such as feminicide, trafficking of girls and women, FGM... 83% 88 Birth control, fertility, sterilization, amniocentesis, termination of pregnancy... 65% 34 Women's participation in economic processes (informal work, paid employment, unemployment, unpaid labour) 77% 55 Sexual harassment against women, rape, sexual assault, #MeToo #TimesUp 79% 122 Beauty contests, models, fashion, beauty aids, cosmetic surgery... 57% 37 Changing gender relations, roles and relationships of women and men inside and outside the home... 60% 15 Chi Id abuse, sexua I violence against chi Idren, neg lect 29% 128 Women politicians, women electoral candidates... 33% 384 Family relations, inter-generational conflict, single parents... 41% 37 Celebrity news, births, marriages, deaths, obituaries, famous people, royalty... 30% 282 Human rights, women's rights, children's rights, gay & lesbian rights, rights of minorities.. 22% 202 Violent crime, murder, abduction, kidnapping, assault, drug-related violence... 24% 662 Family law, family codes, property law, inheritance law and rights... 30% 26 EBOLA, treatment, response... 8% 9 Other stories on social or legal issues (specify the topic in'Comments'section of coding sheet) 12% 270 Media, including new media (computers, internet), portrayal of women and/or men 13% 97 Other stories on celebrities, arts, media (specify the topic in'Comments'section of coding sheet) 22% 55 Other stories on crime and violence (specify the topic in 'Comments' section of coding sheet) 15% 170 Employment 10% 254 Corruption, (including political corruption/malpractice) 9% 359 Legal system, judicial system, legislation (apart from family, property & inheritance law)... 10% 532 Arts, entertainment, leisure, cinema, theatre, books, dance... 11% 425 Non-violent crime, bribery, theft, drug-dealing,... 12% 486 Peace, negotiations, treaties...(local, regional, national), 7% 363 Education,childcare, nursery, university, literacy 7% 698 Other stories on politics and government (specify the topic in 'Comments'section of coding sheet) 8% 301 Sustainable Development Goals (SDGs), Post 2015 agenda, Agenda 2030 5% 44 Use only as a last resort and explain 10% 774 Informal work, street vending,... 4% 47 Sports,events, players, facilities,training, policies,funding... 7% 930 Climate change, global warming 5% 72 Religion, culture, tradition, cultural controversies, teachings, celebrations, practices... 8% 200 Poverty, housing, social welfare, aid to those in need... 7% 294 Fake news, mis-information,dis-information, mal-information... 7% 27 Global partnerships (international trade and finance systems,e.g. WTO, IMF, World Bank, debt)... 4% 138 Riots, demonstrations, public disorder, etc. 4% 223 National defence, military spending, military training, military parades, internal security... 4% 316 Migration, refugees, asylum seekers, ethnic conflict, integration, racism, xenophobia... 7% 135 Other domestic politics/government (local, regional, national), elections, speeches, the political process... 4% 2203 GMMP2020 58 Who Makes the News? Topic % stories n Disaster, accident, famine, flood, plane crash, etc 5% 582 Other epidemics, viruses, contagions, Influenza, BSE, SARS NOT COVID-19 (For stories related to Covid-19 choose the closest relevant sub-topic) 3% 430 Other stones on science or health (specify the topic in 'Comments'section of coding sheet) 3% 180 Medicine, health, hygiene, safety, disability, medical research, funding (not EBOLA or HIV-Al DS)... 4% 1804 War, civil war, terrorism, state-based violence 2% 487 Foreign/international politics, relations with other countries, negotiations, treaties, UN peacekeeping... 4% 987 Science, technology, research, funding, discoveries, developments... 4% 338 Other development issues, sustainability, 2% 209 Rural economy, agriculture, farming practices, agricultural policy, land rights... 3% 316 Other stories on the economy (specify the topic in'Comments'section of coding sheet) 3% 203 Economic policies, strategies, modules, indicators, stock markets, taxes,... 2% 1026 Economic crisis, state bailouts of companies, company takeovers and mergers... 2% 423 Other labour issues, strikes, trade unions, negotiations, other employment and unemployment 2% 201 Transport, traffic, roads... 1% 403 Consumer issues, consumer protection, regulation, prices, consumer fraud... 1% 214 HIV and AIDS, incidence, policy, treatment, people affected... 6% 32 Environment, pollution, tourism... 1% 398 Gender (in)equality in the news Stories that highlight issues concerning equality or inequality between women and men include those that focus directly on an area of inequality. For example, career advancement, wages and salaries, access to resources, or discrimination in relation to rights of various kinds. More stories today raise gender (in)equality issues than 15 years ago albeit two points fewer than in 2015. (Table 57) The Table 57. Stories where issues of gender equality or inequality are raised, by region. 2005-2020. Region 2005 2010 2015 2020 Africa 4% 5% 20% 7% Asia 3% 3% 8% 4% Caribbean 5% 9% 18% 6% Europe 3% 3% 5% 2% Latin America 4% 12% 7% 4% Middle East 1% 4% 6% 3% North America 5% 10% 17% 7% Pacific 3% 1% 1% 5% GLOBAL AVERAGE 4% 6% 9% 7% proportion of such stories has fallen in Europe (-1 point), stagnated in Latin America, and risen by one to three points in the rest of the world. It is disheartening to see the decline on this measure since 2015 at a time of rising gender inequalities as documented in the current global gender gap report (World Economic Forum, 2021). GMMP2020 59 Who Makes the News? Table 58. Stories where gender equality issues are raised, by major topic, by region. 2020. Africa Asia Caribbean Europe Latin America Middle East North America Pacific Politics and Government 12% 6% 12% 4% 5% 3% 14% 9% Economy 16% 9% 9% 4% 7% 4% 17% 6% Science and Health 11% 3% 6% 2% 3% 1% 2% 5% Social and Legal 20% 13% 17% 9% 13% 16% 32% 16% Crime and Violence 13% 5% 17% 3% 6% 6% 13% 12% Gender & Related 48% 24% 45% 35% 34% » » » Celebrity, Arts and Media, Sports 0% 0% 0% 0% 0% 0% 0% 1% 'Too few stories on the topic carried in the major news of the day on the global monitoring day In Table 58 we note how fewer than half of gender-related (sexual harassment, rape, other forms of GBV...) stories actually highlight gender (in)equality issues. This includes less than a quarter of Asian and just over a third of Latin American stories on these topics, the two regions responsible for contributing the bulk of the sample. Less than 1% of celebrity/arts/sports news across the globe except for the Pacific mention gender inequality concerns while social/legal stories are most likely to do so, perhaps due to their legal elements. Fewer than 1% of stories coded in 14 out of the 58 minor topics highlight gender inequality issues, including items on disaster, the informal economy and climate change. (Table 62) Table 59. Stories where issues of gender equality/inequality are raised by major topic. 2005-2020. 2005 2010 2015 2020 A15 yrs Print, radio, television Print, radio, television Print, radio, television Print, radio, television News websites Print, radio, television Politics and Government 3% 3% 8% 6% 9% +3% Economy 1% 4% 7% 8% 5% +7% Science and Health 2% 7% 9% 3% 4% +1% Social and Legal 8% 8% 12% 14% 15% +6% Crime and Violence 4% 5% 10% 6% 5% +2% CeLebrity, Arts and Media, Sports 6% 4% 5% 2% 3% -4% OVERALL 4% 6% 9% 7% 8% +3% GMMP2020 60 Who Makes the News? Table 60. Top 10 news stories in which gender equality issues are most likely to be raised. 2020 Rank Topic Other domestic politics/government (local, regional, national), elections, speeches, the political process... Legal system, judicial system, legislation (apart from family, property & inheritance law)... Human rights, women's rights, children's rights, gay & lesbian rights, rights of minorities.. Economic policies, strategies, modules, indicators, stock markets, taxes,... Education, childcare, nursery, university, literacy Medicine, health, hygiene, safety, disability, medical research, funding (not EBOLA or HIV-AIDS)... Women's movement, feminist activism, events, demonstrations, gender equality advocacy... Women politicians, women electoral candidates... Poverty, housing, social welfare, aid to those in need... Peace, negotiations, treaties...(local, regional, national), Table 61. Raising gender (in)equality issues... the bottom 10 stories. 2020 Rank Topic Beauty contests, models, fashion, beauty aids, cosmetic surgery... HIV and AIDS, incidence, policy, treatment, people affected... Global partnerships (international trade and finance systems, e.g. WTO, IMF, World Bank, debt)... Informal work, street vending,... Climate change, global warming 6 Arts, entertainment, leisure, cinema, theatre, books, dance... Other stories on science or health Celebrity news, births, marriages, deaths, obituaries, famous people, royalty... Consumer issues, consumer protection, regulation, prices, consumer fraud... Other stories on celebrities, arts, media GMMP2020 61 Who Makes the News? Table 62. Stories where issues of gender equaLity/inequaLity are raised by topic-detaiL. 2020 Topic % stories N Other domestic politics/government (Local regional, national), elections, speeches, the political process... 7% 2203 Legal system, judicial system, legislation (apart from family, property & inheritance law)... 7% 532 Human rights, women's rights, children's rights, gay & lesbian rights, rights of minorities.. 7% 202 Economic policies, strategies, modules, indicators, stock markets, taxes,... 5% 1026 Education,childcare, nursery, university, literacy 4% 698 Medicine, health, hygiene, safety, disability, medical research, funding (not EBOLA or HIV-AIDS)... 4% 1804 Women's movement, feminist activism, events, demonstrations, gender equality advocacy... 4% 114 Use only as a last resort and explain 4% 774 Women politicians, women electoral candidates... 4% 384 Poverty, housing, social welfare, aid to those in need... 3% 294 Peace, negotiations, treaties...(local, regional, national), 3% 363 Violent crime, murder, abduction, kidnapping, assault, drug-related violence... 3% 662 Foreign/international politics, relations with other countries, negotiations, treaties, UN peacekeeping... 3% 987 Sexual harassment against women, rape, sexual assault, #MeToo #TimesUp 3% 122 Other stories on politics and government 2% 301 Rural economy, agriculture, farming practices, agricultural policy, land rights... 2% 316 Women's participation in economic processes (informal work, paid employment, unemployment, unpaid labour) 2% 55 Migration, refugees, asylum seekers, ethnic conflict, integration, racism, xenophobia... 2% 135 Child abuse, sexual violence against children, neglect 2% 128 Other development issues, sustainability, 2% 209 Riots, demonstrations, public disorder, etc. 2% 223 Other gender violence such as feminicide, trafficking of girls and women, FGM... 2% 88 Employment 2% 254 Other labour issues, strikes, trade unions, negotiations, other employment and unemployment 2% 201 Other stories on social or legal issues 2% 270 Corruption, (including political corruption/malpractice) 2% 359 National defence, military spending, military training, military parades, internal security... 1% 316 Non-violent crime, bribery, theft, drug-dealing,... 1% 486 Religion, culture, tradition, cultural controversies, teachings, celebrations, practices... 1% 200 Transport, traffic, roads... 1% 403 War, civil war, terrorism, state-based violence 1% 487 Inequality between women and men such as income inequality/gender pay gap, 1% 17 Other stories on crime and violence 1% 170 Media, including new media (computers, internet), portrayal of women and/or men 1% 97 Economic crisis, state bailouts of companies, company takeovers and mergers... 1% 423 Environment, pollution, tourism... 1% 398 Other stories on the economy 1% 203 Birth control, fertility, sterilization, amniocentesis, termination of pregnancy... 1% 34 Changing gender relations, roles and relationships of women and men inside and outside the home... 1% 15 Family relations, inter-generational conflict, single parents... 1% 37 Family law, family codes, property law, inheritance law and rights... 1% 26 GMMP2020 62 Who Makes the News? Topic % stories N Science, technology, research, funding, discoveries, developments... 1% 338 Sustainable Development Goals (SDGs), Post 2015 agenda, Agenda 2030 1% 44 Sports,events, players, facilities,training, policies,funding... 1% 930 Other epidemics, viruses, contagions, Influenza, BSE, SARS. NOT C0VID-19 0% 430 Disaster, accident, famine, flood, plane crash, etc 0% 582 Consumer issues, consumer protection, regulation, prices, consumer fraud... 0% 214 Other stories on celebrities, arts, media 0% 55 Other stories on science or health 0% 180 Celebrity news, births, marriages, deaths, obituaries, famous people, royalty... 0% 282 Global partnerships (international trade and finance systems,e.g. WTO, IMF, World Bank, debt)... 0% 138 Informal work, street vending,... 0% 47 Climate change, global warming 0% 72 Arts, entertainment, leisure, cinema, theatre, books, dance... 0% 425 HIV and AIDS, incidence, policy, treatment, people affected... 0% 32 Beauty contests, models, fashion, beauty aids, cosmetic surgery... 0% 37 Fake news, mis-information,dis-information, mal-information... 0% 27 EBOLA, treatment, response... 0% 9 Does the reporter's gender matter for gender integration in stories? Whether the gender of the reporter matters for the gender quality of stories is an often-debated issue. In 2015 and currently, we have seen a clear gender difference in source selection; there is greater gender diversity of sources in stories by women than in those by men reporters. Story quality from a gender perspective tends to be marginally higher in the output of women journalists, in terms of like- lihood to clearly challenge gender stereotypes (Table 63), to raise gender (in)equality issues (Table 64) and to make reference to legislation or policy that promotes gender equality or human rights (Table 66). Even with the gender difference, it is important not to lose sight of the overall decline or stagnation across time on these indicators in the output of all journalists, women and men alike. Table 63. Gender difference in reporting: On clearly challenging gender stereotypes. 2010-2020. 2010 2015 2020 Women reporters 7% 6% 4% Men reporters 4% 3% 3% Table 64. Gender difference in reporting: On gender (in (equality. 2005 -2020 2005 2010 2015 2020 Women reporters 11% 5% 10% 9% Men reporters 7% 3% 9% 7% GMMP2020 63 Who Makes the News? Table 65. Gender difference in reporting, by region: On gender (in)equaiity. 2015-2020 2015 2020 Women reporters Men reporters Women reporters Men reporters Africa 23% 20% 16% 11% Asia 11% 13% 6% 7% Caribbean 14% 15% 7% 8% Europe 5% 5% 5% 5% Latin America 6% 5% 9% 7% Middle East 11% 6% 9% 4% North America 25% 10% 22% 14% Pacific 3% 2% 11% 9% Table 66. Gender difference in reporting, by major topic: On rights-based journalism. 2015-2020. 2015 Women reporters Men reporters 2020 Women reporters Men reporters Politics and Government 9% 10% 7% 6% Economy 7% 6% 6% 7% Science and Health 6% 5% 3% 3% Social and Legal 13% 11% 14% 11% Crime and Violence 9% 8% 5% 5% CeLebrity, Arts and Media, Sports 4% 4% 2% 2% OVERALL 9% 8% 7% 6% [1] "The IFJ Global Charter of Ethics for Journalists was adopted at the 30th IFJ World Congress in Tunis on 12 June 2019. It completes the IFJ Declaration of Principles on the Conduct of Journalists (1954), known as the "Bordeaux Declaration".The Charter is based on major texts of international law, in particular the Universal Declaration of Human Rights.lt contains 16 articles plus a preamble and defines journalists' duties and rights regarding ethics." httpsy/www.ifj.org/who/rules-and-policy/global-charter-of-ethics-for-journalists.html GMMP2020 64 Who Makes the News? Action Plan 2021-2025 The 2021-2025 Global Action Plan extends actions agreed in 2015 to take into account new challenges that have emerged since the fifth GMMP. PRIORITY ACTIONS For media regulation and self-regulation bodies 1. Integrate or strengthen gender equality provisions in media regulatory tools; 2. Integrate provisions on the issue of violence against women and girls in and through all forms of media, with a clear message on sanctions for flouting regulations; 4. Impose meaningful fines on media outlets found liable for sex discrimination, sexist content or other actions of non-compliance with the gender provisions in regulation; and, 5. Build capacity of staff responsible for hearing cases on media non-compliance with policy and law. 3. Include in media evaluation criteria, gender balance and demonstrated adherence to gender provisions in media law, policy and codes; For media houses 1. Strengthen gender equality dimensions in policy, codes and guidelines, with action plans and clear targets for implementation; 2. Engage with community media organisations and citizens' media networks to advance gender equality in content; 3. Establish gender quotas for senior posts and board positions and monitor progress against targets; 4. Publish gender equality plans and mechanisms for monitoring progress; 5. Publish gender-disaggregated data on job applications, shortlisting and appointment by level; and, 6. Publish gender-disaggregated data on promotions. For civil society 1. Advocacy for news media accountability to women, minority and marginalized groups: i. Advocate for gender provisions in editorial polices of all media organisations; ii. Urge journalists to improve the gender quality of their output and gender balance in sources; iii. Develop issue-based campaigns with an inter-sectional lens on discrimination on the basis of gender, race, ethnicity, disability, age and other applicable axe, iv. Lobby governments to ensure the media's respect for freedom of expression provisions consistent with commitments in international agreements and where applicable, national laws. GMMP 2020 65 Who Makes the News? 2. Permanent gender-focussed media watch: i. Publicize the GMMP results and plan of action; ii. Monitor impact of the GMMP results on media houses; iii. Actively monitor the media and make use of the complaints mechanisms when gender-related ethics and standards are flouted ; iv. Support journalists and media outlets who demonstrate willingness to increase the participation space for women, minority and marginalized groups in and through media; v. Strengthen alliances with national women's machineries and work together to regularise media monitoring; vi. Institute media awards to recognize good practice as well to call out media that outputs sexist content. Use the GMMP scorecard to assess media output and make the results public; and, vii. Increase audience awareness by offering critical, gender-focussed media literacy training. For journalism and media training institutions 1. Capacity building: i. Engage with community media organisations and citizens' media networks to demonstrate and use the GMMP results in training; ii. Incorporate gender training in journalism education and professional development; iii. Build capacity of news journalists about gender sensitive news reporting; Increase journalists' understanding of the impacts of biased reporting; and, For funding agencies 1. Extend meaningful funding to civil society groups working on gender, media and communication. 2. Support the strengthening or establishment of media watch networks. 3. Support media development initiatives that emphasizes accountability to women, minority and marginalized groups in content, media policy and practice. For researchers 1. Adapt and use the GMMP methodology and tools to expand and deepen knowledge on gender and media. 2. Work with journalists associations and unions to research on gender concerns in the profession. 3. Exploit the data gathered by the GMMP and others to build and strengthen knowledge on the gender and the media. 3. Other i. Nurture and strengthen relations with supportive journalists, editors and other news media professionals. Regularly update them on issues, concerns and events for publication in their respective media outlets; ii. Provide media houses with lists of experts available to provide commentary on the topics in which they work. Sign up on experts' lists where available. iv. Build journalists skills to navigate the structural barriers that impede gender responsive reporting. GMMP 2020 66 Who Makes the News? Annex 1 References Askanius, Tina, and Jannie Moller Hartley, n.d. "Framing Gender Justice: A Comparative Analysis of the Media Coverage of #metoo in Denmark and Sweden." Nordicom Review 40 (2): 19-36. https://doi.Org/https://doi.org/10.2478/ nor-2019-0022. Baker, Andrea, Katrina Williams, and Usha M Rodrigues. 2020. "#metoo 2.0 to #meNOmore: Analysing Western Reporting About Sexual Violence in the Music Industry." Journalism Practice 14(2): 191-207. https://doi.org/10.1080 /17512786.2019.1674683. Benedictis, Sara De, Shani Orgad, and Catherine Rotten-berg. 2019. "#MeToo, Popular Feminism and the News : A Content Analysis of UK Newspaper Coverage." European Journal of Cultural Studies 22 (5-6): 718-38. https://doi. org/10.1177/1367549419856831. Bloomfield, Emma Frances. 2019. "Rhetorical Constellations and the Inventional/Intersectional Possibilities of #MeToo." Journal of Communication Inquiry 43 (4): 394-414. https://doi.org/10.1177/0196859919866444. Blumell, Lindsey E. 2019. "She Persisted... and so Did He." Journalism Studies 20 (2): 267-86. https://doi.org/10.1080/ 1461670X.2017.1360150. Bridges, Donna, and Ben Wadham. 2020. "Gender under Fire: Portrayals of Military Women in the Australian Print Media." Feminist Media Studies 20 (2): 219-37. https://doi. org/10.1080/14680777.2019.1592208. Field, Anjalie, Gayatri Bhat, and Yulia Tsvetkov. 2019. "Contextual Affective Analysis: A Case Study of People Portrayals in Online #MeToo Stories." ArXiv:1904.04164vl [Cs.SI]. https://arxiv.org/pdf/1904.04164.pdf. Foster, Johanna E, and Sherizaan Minwalla. 2018. "Voices of Yazidi Women: Perceptions of Journalistic Practices in the Reporting on ISIS Sexual Violence." Women's Studies International Forum 67: 53-64. https://doi.Org/https://doi. org/10.1016/j.wsif.2018.01.007. Hernandez, Miriam. 2017. "'Killed Out of Love': A Frame Analysis of Domestic Violence Coverage in Hong Kong." Violence Against Women 24 (12): 1454-73. https://doi. org/10.1177/1077801217738581. Hindes, Sophie, and Bianca Fileborn. 2020. "'Girl Power Gone Wrong': #MeToo, Aziz Ansari, and Media Reporting of (Grey Area) Sexual Violence." Feminist Media Studies 20 (5): 639-56. https://doi.org/10.1080/14680777.2019.1606 843. Hines, Revathi I. 2007. "Natural Disasters and Gender Inequalities: The 2004 Tsunami and the Case of India." Race, Gender & Class 14 (1/2): 60-68. http://www.jstor.org/ stable/41675195. Jia, Sen, Thomas Lansdall-Welfare, Saatviga Sudhahar, Cynthia Carter, and Nello Cristianini. 2016. "Women Are Seen More than Heard in Online Newspapers." PloS One 11 (February): e0148434. https://doi.org/10.1371/journal. pone.0148434. Lobo, Paula, Maria Joao Silveirinha, Marisa Torres da Silva, and Filipa Subtil. 2017. "'In Journalism, We Are All Men.'" Journalism Studies 18 (9): 1148-66. https://doi.org/10.1080 /1461670X.2015.1111161. McKinnon, Scott, Andrew Gorman-Murray, and Dale Dominey-Howes. 2017. "Disasters, Queer Narratives, and the News: How Are LGBTI Disaster Experiences Reported by the Mainstream and LGBTI Media?" Journal of Homosexuality 64 (1): 122-44. https://doi.org/10.1080/00918369.20 16.1172901. Mishra, Suman. 2020. "From #MeToo to #MeTooIndia: News Domestication in Indian English Language Newspapers." Journalism Studies 21 (5): 659-77. https://doi.org/10. 1080/1461670X.2019.1709882. Mittal, Shalini, and Tushar Singh. 2020. "Gender-Based Violence During COVID-19 Pandemic: A Mini-Review ." Frontiers in Global Women's Health . https://www.frontier-sin.org/article/10.3389/fgwh.2020.00004. Nilsson, Gabriella. 2019. "Rape in the News: On Rape Genres in Swedish News Coverage." Feminist Media Studies 19 (8): 1178-94. https://doi.Org/10.1080/14680777.2018.l 513412. North, Louise. 2016. "The Gender of 'Soft' and 'Hard' News." Journalism Studies 17 (3): 356-73. https://d0i.0rg/l 0.1080/1461670X.2014.987551. O'Boyle, Jane, and Oueenie Jo-Yun Li. 2019. "#MeToo Is Different for College Students: Media Framing of Campus Sexual Assault, Its Causes, and Proposed Solutions." Newspaper Research Journal 40 (4): 431-50. https://doi. org/10.1177/0739532919856127. Owusu-Addo, Ebenezer, Sally B. Owusu-Addo, Ernestina F. Antoh, Yaw A. Sarpong, Kwaku Obeng-Okrah, and Grace K. Annan. 2018. "Ghanaian Media Coverage of Violence against Women and Girls: Implications for Health Promotion." BMC Women's Health 18 (1): 1-11. https://doi. org/10.1186/sl2905-018-0621-l. Rojas Rajs, Maria Soledad. 2014. "La Violencia Contra Las Mujeres En Las Noticias En Mexico: El Tratamiento Acritico y Dispar de Un Grave Problema Social 1." Acta Soci-ologica 65: 37-64. https://doi.Org/https://doi.org/10.1016/ S0186-6028(14)70236-l. Ross, Karen, Karen Boyle, Cynthia Carter, and Debbie Ging. 2018. "Women, Men and News." Journalism Studies 19 (6): 824-45. https://doi.org/10.1080/1461670X.2016.1222884. Seager, Joni. 2006. "Noticing Gender (or Not) in Disasters." Geoforum 37: 2-3. https://www.d.umn.edu/~pfarrell/Natu-ral Hazards/Readings/S eager article.pdf. Sela-Shayovitz, Revital. 2018. '"She Knew He Would Murder Her': The Role of the Media in the Reconstruction of Intimate Femicide." Journal of Comparative Social Work 13 (1): 11-34. https://doi.org/10.31265/jcsw.vl3il.157. GMMP2020 67 Who Makes the News? Starkey, Jesse C, Amy Koerber, Miglena Sternadori, and Bethany Pitchford. 2019. "#MeToo Goes Global: Media Framing of Silence Breakers in Four National Settings." Journal of Communication Inquiry 43 (4): 437-61. https:// doi.org/10.1177/0196859919865254. Sutherland, Georgina, Patricia Easteal, Kate Holland, and Cathy Vaughan. 2019. "Mediated Representations of Violence against Women in the Mainstream News in Australia." BMC Public Health 19 (502). https://doi.org/10.1186/ S12889-019-6793-2. Tambe, Ashwini. 2018. "Reckoning with the Silences of #MeToo." Feminist Studies 44 (1): 197-202. Tyree, Tia, and Marcus Hill. 2016. "Hurricane Katrina 10 Years Later: A Qualitative Meta-Analysis of Communications and Media Studies of New Orleans' Black Community." International Journal of Emergency Management 12 (January): 304. https://doi.org/10.1504/IJEM.2016.079021. Usher, Nikki, Jesse Holcomb, and Justin Littman. 2018. "Twitter Makes It Worse: Political Journalists, Gendered Echo Chambers, and the Amplification of Gender Bias." The International Journal of Press/Politics 23 (3): 324-44. https://doi.org/10.1177/1940161218781254. Waring, Marilyn. 1988. Counting for Nothing: What Men Value & What Women Are Worth /Marilyn Waring. Wellington, N.Z: Allen & Unwin/Port Nicholson Press. World Economic Forum. 2015. "Global Gender Gap Report." http://www3.weforum.org/docs/GGGR2015/The Global Gender Gap Index 2015.pdf. ---. 2021. "Global Gender Gap Report." Geneva, http:// www3.weforum.org/docs/WEF_GGGR_2021.pdf. Wright, Scott. 2011. "Politics as Usual? Revolution, Normalization and a New Agenda for Online Deliberation." New Media & Society 14 (2): 244-61. https://doi. org/10.1177/1461444811410679. GMMP2020 Annex 2 Methodology expanded discussion Twenty-five years after the Fourth UN World Conference on Women in Beijing, and in the middle of the Covid-19 pandemic, hundreds of volunteers from 116 countries came together on 29 September 2020, to answer the question: What does a snapshot of gender in one 'ordinary' news day look like? From Papua New Guinea to the Cayman Islands, volunteers monitored close to 30,000 news stories from print, broadcast, online and social media platforms, contributing to the world's largest and longest running longitudinal study on gender representation in the media. There were teams from countries that have been part of the project since the beginning in 1995, teams from nations returning after a long absence such as Russian Federation, and others who represented, for the first time, countries such as the Central African Republic, Myanmar and Iraq. A key characteristic of longitudinal research is the assessment of changes over time in the observed variables. Longitudinal studies in general seek to identify, among others, changes in attitudes, behaviours and societal perceptions. They also seek to quantify the impact of particular exposures/events on the observed variables, in this case, changes in the representation of women and men in news media. As such, the methods of data collection and analysis must remain consistent over time to accurately capture any systemic changes. Similarly, the methodology and indicators studied have remained relatively stable over the years, to enable historical comparisons. This is a guide to the research and analysis methodology utilised in the 2020 GMMP. How the monitoring took place The global monitoring day scheduled initially for the first quarter of 2020 was postponed due to the upheavals caused by the first Covid-19 wave across the globe. As the monitoring day originally set for April 2020 approached, it became clear that proceeding as planned would result in a news sample that would be almost entirely focused on coronavirus stories. Anew need emerged to address the practicalities of monitoring amidst the lockdowns and curfews imposed to contain the spread of the virus, as the regular sit-down communal coding sessions were now out of the question for most teams. The risks to health and livelihoods, the need to find ways of coping with the new reality would shift the GMMP down on the ladder of priorities for the volunteers, potentially increasing the drop-out rate. These new challenges called for a pause on the plans, to search for solutions, and put in place the tools and resources necessary before monitoring could proceed. The GMMP technical advisory group and Code for Africa, the platform development team, worked to systematically address the issues. A new monitoring date was set for September, the coding tools were tweaked to capture Covid-19 stories without compromising on the ability to compare results across time, based on story topics, exhaustive audio-visual training resources on how to code in a pandemic were put in place, electronic coding instruments were developed and the teams were re-trained through numerous webinars. As with previous editions of the GMMP, the initial data capture was conducted offline by volunteer teams across the 116 participating countries. For the 2020 GMMP, a spreadsheet version of the coding sheets was provided, to allow for electronic recording of the observations. In the period leading up to the monitoring day, regional and national training sessions were organised to build a uniform understanding of the teams on the methodology and approach to coding. The teams received training on media selection, newscast and article selection, the number of media to code and how to select each country's contextual information. For the 2020 GMMP, countries could choose from two possible options for the monitoring: • Full monitoring, whose results provide a comprehensive picture of the status of gender equality dimensions in news media. • Short monitoring, a shorter version which focuses on the key GMMP indicators, for teams who wish to take part but might be constrained from implementing the full monitoring. To ensure accuracy in the coding process, each radio and television bulletin was recorded, and copies of digital and print media pieces were collected for reference purposes. Across the different media types (both for full and short monitoring), monitors captured information about the story, its main themes and the people in the story, both as journalists and story subjects. Additionally, three optional special questions, unique to each country, allowed individual countries to analyse issues of national interest. For standardisation purposes, as well as the multilingual nature of this study, all responses were numerically coded from fixed lists. A concern raised by many teams was the possible homogeneity of news topics as a result of the pandemic, which would affect historical comparisons. To enable comparability with the historical results, we included an additional GMMP 2020 69 Who Makes the News? question across the five media types, which asked whether the story was related to Covid-19. For such stories, monitors were requested to code the most relevant secondary topic. While global news stories had diversified to pre-pan-demic levels by the global monitoring day, the regional analysis demonstrated the significance of this question, particularly for North America and the Middle East, which recorded 37% and 36% of Covid-19 stories respectively. How media bands were created The media bands system was introduced in 2005 to ensure a more even spread of data and also serve as each country's reference point on the minimum number of media to monitor. This system was retained for the 2020 GMMP and was developed with the input of the country coordinators. The participating countries were ranked according to the number of their newspapers, radio and television news stations and then grouped into media bands. For example, a country with 4 radio stations was placed in band 3 and expected to monitor broadcasts from at least 3 radio stations, while a country with 10 radio stations belonged to band 5 and was expected to monitor at least 8 stations. For internet and Twitter news, countries were ranked according to internet penetration rates published by the International Telecommunication Union (ITU) and also grouped into media bands. Due to demand, an upper limit of the number of media to monitor was removed; teams could monitor as many media as they wished but they needed to observe the minimum recommended. In selecting the information sources to update on the media bands list, we considered the following: • Reputability of the source - We selected data from organisations/data collection agencies with experience and capability in media data collection. • Ease of access and completeness - To maintain transparency in the media density banding, we selected accessible data sources which can be easily verified by the country and regional coordinators. Additionally, the selected data sources needed to be complete without significant missing data, as these gaps would give a partial view of the media density. • Timeliness - To ensure the number of outlets monitored are an accurate representation of each country's media density, we selected data sources that had been updated at least once since 2015. The information used to update the media bands, which was supplemented by submissions from national coordinators, was sourced from the following global and regional sources: International Federation of Audit Bureaux of Certification (IFABC) Certified Media List(2020) MAVISE Database on audiovisual services in Europe(2020) IREX's Media Sustainability Index (2019) NORDICOM Media Statistics(2019) Reporters Without Borders' Media Ownership Monitor (MOM) (2018 and 2019) How media weights were created While the GMMP seeks to understand how gender is represented in media across the world, differences in media access and impact across the participating countries mean that a simple aggregation of the data would lead to biased results. For example, if a country like France submitted data from 100 media, the entries from a smaller country like Fiji would have little, if any, impact on the results. Therefore, the results need to be normalised to ensure that each country's results have the same impact on the global results. Additionally, while two countries may have a similar number of newspapers, their impact, in terms of the number of people who read them, may be significantly different. To address these challenges, GMMP 2020 updated, re-tested and applied the weighting system first developed for the 2005 edition. The weights used to produce all global and regional results are based on: : • Media circulation, which accounts for the impact of each media type. For print media, published (or reasonable estimate) newspaper readership statistics were used while internet penetration rates were used for digital media; • Country population size, to account for media reach; Sampling, to adjust for the number of each medium monitored in relation to the recommended sample size As with previous GMMPs, a square root system common to transnational research was applied to prevent the introduction of a skew in the results due to wide disparities in population sizes. This ensured that large countries like India with almost 18% of the world's population did not determine the final result, and that data from the smallest countries such as Dominica counted. How accuracy was guaranteed The GMMP involved several thousand people across 116 countries from diverse gender and media stakeholder groups, with different research abilities and working in a wide range of languages. For a study of this scale, it was crucial that accuracy was considered at each stage, to maintain the high levels achieved in previous years. Data entry and processing errors can have severe biasing effects on the data analysis, resulting in misrepresentation of the observed variables. To minimise this risk, we leveraged on a variety of automated processes, as well as the extensive media monitoring experience of the country coordinators. As with the 2020 GMMP, the data capture platform was fully online. The platform was designed to follow the same structure as the coding sheets and included a new language selection feature, allowing participants to access the platform in English, French or Spanish. To minimise the risk of data formatting errors, the platform utilised drop down responses. For example, according to the coding guide, a question on the story's reference to gender and human rights could either have a 'yes' or 'no' response. Any attempt to input another value resulted in a prompt to rectify the response. The platform also included a feature to detect input errors on dependent questions - those whose responses were dependent on a previous question. For example, the full monitoring version asked whether the story subject was identified as a victim or survivor. If the answer was in the affirmative, then two additional questions would be dis- GMMP2020 70 Who Makes the News? played. If the story was saved without these responses, an error notification would alert the monitor to these missing responses. This error notification functionality was also used to alert the monitor if they had omitted responses on any of the mandatory questions. To further minimise data entry errors, we automated the upload process for the spreadsheet versions of the coding sheets. analysts, to verify adherence to the sampling methodology. All inconsistencies were flagged for clarification and updates. In most cases, the submitted coding sheets followed the GMMP methodology and were included in the final analysis. Cases that failed to meet the sampling criteria were excluded from the final analysis, to maintain the reliability of the study. During the data upload process, we regularly generated analysis reports and compared these inputs with the coding sheets. Even with the various automated data quality checks in place, the quality assurance process relied on the GMMP country coordinators and Code for Africa's team of data Limitations As with any study, great effort was made to ensure accuracy of the data. As observed in previous GMMPs, an exact error of measurement cannot be determined due to the study's magnitude. Conventional error measurement would involve different researchers coding the same story and then calculating a level of error from the differences between the results. Although this was not possible for GMMP, we followed best practice mechanisms to make sure that there were minimal errors in the data capture and analysis generation process. Code for Africa (CfA) is the continent's largest network of civic technology and data journalism labs, with teams in 21 countries. CfAbuilds digital democracy solutions that give citizens unfettered access to actionable information that empowers them to make informed decisions, and that strengthens civic engagement for improved public governance and accountability. This includes building infrastructure like the continent's largest open data portals at openAFRICA and sourceAFRICA, as well as incubating initiatives as diverse as the africanDRONE network, the PesaCheck fact-checking initiative and the sensors.AFRICA air quality sensor network. CfA also manages the African Network of Centres for Investigative Reporting (ANCIR), which gives the continent's best muckraking newsrooms the best possible forensic data tools, digital security and whistleblower encryption to help improve their ability to tackle crooked politicians, organised crime and predatory big business. CfA also runs one of Africa's largest skills development initiatives for digital journalists, and seed funds cross-border collaboration. Lead Technologist: Clemence Kyara Backend technologist: Isaiah Ngaruiya Data Analysts : Tricia Govindasamy, Mercy Karagi, Zahara Tunda, Emma Kisa and Jean Githae Special thanks : Catherine Gicheru, David Lemayian, Samuel Afolaranmi and Yazmin Jumaali 71 About Code for Africa Credits Annex 3 List of topics Politics and Government 1. Women politicians, women electoral candidates,... 2. Peace, negotiations, treaties...(local, regional, national), 3. Other domestic politics/government (local, regional, national), elections, speeches, the political process ... 4. Global partnerships (international trade and finance systems, e.g. WTO, IMF, World Bank, debt)... 5. Foreign/international politics, relations with other countries, negotiations, treaties, UN peacekeeping ... 6. National defence, military spending, military training, military parades, internal security ... 7. Other stories on politics and government (specify the topic in 'Comments' section of coding sheet) Economy 8. Economic policies, strategies, modules, indicators, stock markets, taxes,... 9. Economic crisis, state bailouts of companies, company takeovers and mergers ... 10. Poverty, housing, social welfare, aid to those in need ... 11. Women's participation in economic processes (informal work, paid employment, unemployment, unpaid labour) 12. Employment 13. Informal work, street vending,... 14. Other labour issues, strikes, trade unions, negotiations, other employment and unemployment 15. Rural economy, agriculture, farming practices, agricultural policy, land rights ... 16. Consumer issues, consumer protection, regulation, prices, consumer fraud ... 17. Transport, traffic, roads...... 18. Other stories on the economy (specify the topic in 'Comments' section of coding sheet) Science and Health 19. Science, technology, research, funding, discoveries, developments ... 20. Medicine, health, hygiene, safety, disability, medical research, funding (not EBOLA or HIV-AIDS)... 21. EBOLA, treatment, response... 22. HIV and AIDS, incidence, policy, treatment, people affected ... 23. Other epidemics, viruses, contagions, Influenza, BSE, SARS. NOT COVID-19 (For stories related to Covid-19 choose the closest relevant sub-topic)... 24. Birth control, fertility, sterilization, amniocentesis, termination of pregnancy ... 25. Climate change, global warming 26. Environment, pollution, tourism ... 27. Other stories on science or health (specify the topic in 'Comments' section of coding sheet) GMMP2020 72 Who Makes the News? Social and Legal 28. Sustainable Development Goals (SDGs), Post 2015 agenda, Agenda 2030 29. Family relations, inter-generational conflict, single parents ... 30. Human rights, women's rights, children's rights, gay & lesbian rights, rights of minorities .. 31. Religion, culture, tradition, cultural controversies, teachings, celebrations, practices ... 32. Migration, refugees, asylum seekers, ethnic conflict, integration, racism, xenophobia ... 33. Other development issues, sustainability, 34. Education, childcare, nursery, university, literacy 35. Women's movement, feminist activism, events, demonstrations, gender equality advocacy ... 36. Changing gender relations, roles and relationships of women and men inside and outside the home ... 37. Family law, family codes, property law, inheritance law and rights ... 38. Legal system, judicial system, legislation (apart from family, property & inheritance law) ... 39. Disaster, accident, famine, flood, plane crash, etc 40. Riots, demonstrations, public disorder, etc 41. Other stories on social or legal issues (specify the topic in 'Comments' section of coding sheet) Crime and Violence 42. Non-violent crime, bribery, theft, drug-dealing,... 43. Corruption, (including political corruption/malpractice) 44. Violent crime, murder, abduction, kidnapping, assault, drug-related violence ... 45. Child abuse, sexual violence against children, neglect 46. War, civil war, terrorism, state-based violence 47. Other stories on crime and violence (specify the topic in 'Comments' section of coding sheet) Gender and related 48. Sexual harassment against women, rape, sexual assault, #MeToo, #TimesUp 49. Other gender violence such as feminicide, trafficking of girls and women, FGM... 50. Inequality between women and men such as income inequality/gender pay gap, Celebrity, Arts and Media, Sports 51. Celebrity news, births, marriages, deaths, obituaries, famous people, royalty ... 52. Arts, entertainment, leisure, cinema, theatre, books, dance ... 53. Media, including new media (computers, internet), portrayal of women and/or men 54. Fake news, mis-information, dis-information, mal-information... 55. Beauty contests, models, fashion, beauty aids, cosmetic surgery ... 56. Sports, events, players, facilities, training, policies, funding ... 57. Other stories on celebrities, arts, media (specify the topic in 'Comments' section of coding sheet) Other 58. Use only as a last resort and explain GMMP2020 73 Who Makes the News? Annex 4 Participating teams and data sample News website stories Newspaper stories Radio stories Television stories News Media Tweets Antigua and Barbuda 4 14 3 Argentina 53 95 87 38 168 Australia 99 140 73 166 92 Austria 87 29 18 17 Bangladesh 23 144 23 62 Belgium 54 75 36 89 67 Benin 33 25 9 Bolivia 69 59 85 295 83 Bosnia and Herzegovina 87 70 49 67 14 Botswana 31 32 1 18 Brazil 64 65 66 106 70 Bulgaria 27 23 15 26 8 Burkina Faso 20 63 58 43 Cambodia 26 3 12 Cameroon 30 49 32 51 11 Canada 63 103 109 103 65 Cayman Islands 24 12 4 Central African Republic 9 13 Chad 6 9 7 22 10 Chile 44 87 101 87 China 711 368 186 388 188 Mainland China (PRC) 479 102 91 136 93 Macao SAR (PRC) 43 80 25 32 Hong Kong SAR (PRC) 84 102 54 38 95 Taiwan Province of China 105 84 16 182 Colombia 42 37 103 25 Congo 1 4 3 1 Congo (Democratic Republic of the) 15 26 111 24 Costa Rica 100 65 62 109 77 Cuba 52 19 50 37 47 Cyprus 14 48 29 123 16 Denmark 74 79 61 24 Dominica 3 8 12 Dominican Republic 48 49 12 24 24 Ecuador 73 50 29 119 71 Egypt 124 63 59 32 El Salvador 25 21 35 43 30 74 News website stories Newspaper stories Radio stories Television stories News Media Tweets Estonia 31 50 34 Ethiopia 10 29 9 78 Eswatini 140 4 Fiji 25 29 25 13 Finland 95 112 28 35 95 France 62 96 294 67 139 Gabon 14 Gambia 1 26 2 6 4 Georgia 58 459 167 Ghana 97 192 180 165 209 Greenland 24 44 26 7 Grenada 13 11 20 Guatemala 74 61 99 61 96 Guinea 1 14 4 Guyana 10 11 6 12 Haiti 28 4 83 20 19 Iceland 91 27 30 23 India 96 273 56 157 153 Indonesia 12 23 44 Irag 1 8 Ireland 47 81 37 40 60 Israel 76 36 23 45 44 Italy 106 107 57 56 146 Jamaica 35 39 40 47 35 Japan 14 45 72 47 Jordan 116 105 59 122 Kenya 13 65 15 25 Kyrgyzstan 73 72 36 50 Lebanon 72 21 23 36 76 Luxembourg 62 53 29 14 42 Malawi 11 53 51 42 Malaysia 81 97 37 147 29 Mali 7 88 24 17 10 Malta 119 109 20 56 98 Mexico 136 122 272 191 287 Moldova 60 17 59 112 61 Mongolia 48 48 31 54 30 Morocco 61 48 32 45 Myanmar 25 39 21 36 Namibia 36 5 Nepal 64 166 103 72 29 Netherlands 43 73 23 25 37 75 News website stories Newspaper stories Radio stories Television stories News Media Tweets New Zealand 47 53 22 39 71 Nicaragua 18 13 41 41 Niger 12 12 7 17 Nigeria 73 76 36 67 59 Norway 38 78 57 45 68 Pakistan 54 117 12 143 57 Palestine 53 53 27 36 Papua New Guinea 12 24 14 35 Paraguay 29 70 52 46 35 Peru 60 60 86 180 Poland 61 55 118 147 65 Portugal 64 54 32 99 80 Puerto Rico 77 35 23 47 74 Romania 168 96 66 109 10 Russian Federation 175 82 Senegal 36 11 15 16 Serbia 68 56 42 98 40 Seychelles 6 21 8 Sierra Leone 3 3 1 South Africa 16 80 18 35 South Sudan 20 15 13 Spain 77 81 93 222 90 Suriname 37 6 42 30 15 Sweden 77 142 30 42 Switzerland 165 212 56 58 162 Tanzania 6 89 38 63 22 Togo 17 28 40 21 Trinidad and Tobago 36 6 17 9 Tunisia 63 76 95 81 Turkey 147 128 216 337 188 Uganda 43 33 38 United Kingdom 177 271 64 145 168 United States of America 41 127 19 42 14 Uruguay 36 44 158 160 104 Venezuela 50 40 95 127 117 Vietnam 8 24 11 11 Zambia 11 12 4 9 Zimbabwe 25 78 7 13 GMMP2020 76 Who Makes the News? Annex 5 Data tables 1. Gender equality in news media content index (GEM-I). 2020 2. Sex of presenters, reporters and news subjects & sources in newspaper, television and radio news 3. Subjects & sources in newspaper, television and radio news 4. Subjects & sources in newspaper, television and radio news, by major topic areas 5. Subjects & sources in newspaper, television and radio news, by major occupational groups 6. Function of subjects & sources in newspaper, television and radio news 7. Subjects & sources in newspaper, television and radio news described as victims 8. Subjects & sources in newspaper, television and radio news, mentioned by family status 9. Subjects & sources quoted directly in newspapers 10. Subjects & sources appearing in newspaper photographs 11. Presenters and reporters in newspaper, television and radio news 12. Reporters in print, televisio and radio news, by major topic areas 13. Subject and source selection by sex, by sex of reporter in print, television and radio stories 14. This story clealy challenges gender stereotypes. Responses on print, television and radio news 15. This story clearly highlights issues of gender equality or inequality. Responses on print, television and radio news 16. This story quotes or makes reference to legislation or policy that promotes gender equality or human rights. Responses on print, radio and television news. 17. News websites and news media tweets. Sex of reporters and news subjects & sources 18. News websites and news media tweets. News subjects & sources, by sex 19. News websites and news media tweets. News subjects & sources in major topic areas, by sex 20. News websites. Subjects & sources in major occupational groups, by sex 21. News websites. Function of subjects & sources, by sex 22. News websites. Subjects & sources described as victims, by sex 23. News websites. Subjects and sourcs who are quoted directly, by sex 24. News websites and news media tweets. Subjects & sources appearing in images and video plug-ins, by sex 25. News websites and news media tweets.Reporters in major topic areas, by sex 26. News websites and news media tweets. Responses to "This story cleary challenges gender stereotypes" GMMP2020 77 Who Makes the News? 1. Gender equality in news media content index (GEM-I). 2020 The GEM-Index is a unitary measure of the level of gender equality in news media content and it is constructed to be theoretically informed, easy to apply and rate, broadly applicable to all forms of news media, and unidimen-sional and reliable in statistical terms. The index includes six indicators from the GMMP and considers the overall presence of women and men in the news, as well as their visibility and voice in specific gender sensitive roles and topics. The GEM-Index calculates the average gender gap in the news (percentage of women - percentage of men) for the following six indicators: (1) all news subjects or sources ('people in the news'), (2) reporters, (3) news subjects or sources in economy and business news, (4) news subjects or sources in news about politics and government, (5) spokespersons and (6) experts. The GEM-I can vary between -100 (only men in the news) and + 100 (only women in the news). Zero (0) represents full gender equality and a 50/50 distribution of men and women for all six indicators (see Djerf-Pierre & Edstrom, 2020 for an extensive description of the construction of the index). GEM-I GEM-I Argentina -53.063 Gambia -65.553 Australia -36.007 Georgia -34.607 Austria -50.953 Ghana -67.836 Bangladesh -71.337 Greenland -14.625 Belgium -45.572 Grenada -30.029 Benin -35.089 Guatemala -52.267 Bolivia -47.104 Guyana -82.721 Bosnia and Herzegovina -50.808 Haiti -67.207 Botswana -40.842 Hong Kong SAR (PRC) -47.182 Brazil -49.878 Iceland -38.190 Bulgaria -28.482 India -77.355 Burkina Faso -65.502 Indonesia -68.860 Cambodia -49.098 Ireland -47.764 Cameroon -53.701 Israel -79.829 Canada -35.415 Italy -49.197 Cayman Islands -13.506 Jamaica -27.139 Central African Republic -27.022 Japan -70.320 Chad -41.554 Jordan -50.488 Chile -42.225 Kenya -61.911 Mainland China (PRC) -45.278 Kyrgyzstan -47.445 Colombia -50.534 Lebanon -57.808 Congo (Democratic Republic of the) -53.419 Luxembourg -53.640 Costa Rica -39.342 Macao SAR (PRC) -46.297 Cuba -38.906 Malawi -50.867 Cyprus -56.960 Malaysia -62.137 Denmark -35.371 Mali -71.249 Dominican Republic -55.805 Malta -42.261 Ecuador -46.391 Mexico -48.014 Egypt -58.781 Moldova -24.913 El Salvador -39.636 Mongolia -43.074 Estonia -50.679 Morocco -66.260 Eswatini -31.539 Myanmar -56.921 Ethiopia -72.433 Namibia -32.732 Fiji -39.720 Nepal -61.309 Finland -27.975 Netherlands -43.379 France -47.239 New Zealand -16.653 GMMP 2020 78 Who Makes the News? GEM-I GEM-I Nicaragua -4.598 Nigeria -72.729 Norway -35.833 Pakistan -77.370 Palestine -71.131 Papua New Guinea -57.410 Paraguay -66.246 Peru -47.303 Poland -53.942 Portugal -29.349 Puerto Rico -25.040 Romania -26.632 Russian Federation -44.336 Senegal -68.394 Serbia -48.891 Seychelles -14.878 South Africa -17.386 South Sudan -72.513 Spain -27.235 Suriname -33.772 Sweden_-25.961 Switzerland -46.908 Taiwan Province of China -37.985 Tanzania -41.241 Togo -45.949 Trinidad and Tobago -35.918 Tunisia_-52.999 Turkey -68.697 Uganda -48.649 United Kingdom -44.707 United States of America -29.391 Uruguay -56.410 Venezuela_-59.130 Vietnam -50.153 Zimbabwe -57.665 2. Sex of presenters, reporters and news subjects & sources in newspaper, television and radio news PRESENTER REPORTER SUBJECTS & SOURCES APPENDIX 5-2 Female % N Male % N Female % N Male % N Female % N Male % N Antigua and Barbuda 44% 7 56% 9 100% 5 0% 0 17% 4 83% 19 Argentina 37% 56 63% 96 48% 50 52% 55 20% 128 80% 510 Australia 67% 165 33% 82 44% 107 56% 134 32% 371 68% 805 Austria 74% 28 26% 10 40% 21 60% 31 25% 26 75% 79 Bangladesh 74% 64 26% 23 11% 10 89% 82 16% 100 84% 510 Belgium 44% 63 56% 79 36% 42 64% 74 26% 113 74% 321 Benin 44% 17 56% 22 20% 9 80% 36 28% 40 72% 104 Bolivia 48% 140 52% 150 42% 107 58% 146 24% 179 76% 558 Bosnia and Herzegovina 98% 112 2% 2 47% 42 53% 47 20% 81 80% 334 Botswana 48% 14 52% 15 41% 13 59% 19 27% 14 73% 38 Brazil 50% 110 50% 108 46% 96 54% 111 27% 197 73% 526 Bulgaria 20% 6 80% 24 67% 18 33% 9 32% 27 68% 57 Burkina Faso 44% 46 56% 59 32% 53 68% 114 17% 57 83% 284 Cambodia 50% 6 50% 6 20% 6 80% 24 34% 27 66% 52 Cameroon 26% 21 74% 60 46% 46 54% 54 19% 49 81% 211 Canada 52% 93 48% 85 41% 90 59% 130 31% 213 69% 482 Cayman Islands 100% 16 0% 0 55% 6 45% 5 40% 26 60% 39 Centra I African Republic 22% 2 78% 7 43% 10 57% 13 12% 4 88% 29 Chad 0% 0 0% ~~ol 19% 7 81% 29 30% 11 70% 26 Chile 37% 52 63% 87 37% 61 63% 105 26% 221 74% 641 People's Republic of China 53% 116 47% 102 J 57% 72 43% 55 27% 85 73% 225 Colombia 23% 12 77% 40 ] 39% 33 61% 52 23% 57 77% 196 79 PRESENTER REPORTER SUBJECTS & SOURCES APPENDIX 5-2 Female % N Male % N Female % N Male % N Female % N Male % N Congo 100% 4 0% 0 67% 2 33% 1 100% 8 0% 0 Congo (Democratic Republic of 46% 30 54% 35 37% 34 63% 58 20% 36 80% 144 the) Costa Rica 44% 84 56% 105 41% 70 59% 99 30% 140 70% 325 Cuba 40% 24 60% 36 59% 36 41% 25 21% 50 79% 183 Cyprus 41% 65 59% 92 51% 77 49% 74 21% 92 79% 354 Denmark 53% 41 47% 37 21% 16 79% 59 35% 108 65% 201 Dominica 0% 0 100% 12 100% 2 0% 0 33% 6 67% 12 Dominican Republic 59% 22 41% 15 54% 27 46% 23 24% 44 76% 138 Ecuador 21% 27 79% 99 45% 50 55% 61 24% 125 76% 403 Egypt 34% 33 66% 65 70% 21 30% 9 12% 27 88% 190 El Salvador 24% 13 76% 41 56% 25 44% 20 25% 51 75% 157 Estonia 44% 21 56% 27 36% 20 64% 35 25% 31 75% 93 Eswatini 100% 4 0% 0 52% 48 48% 44 27% 24 73% 64 Ethiopia 34% 35 66% 67 28% 19 72% 48 11% 16 89% 124 Fiji 34% 13 66% 25 54% 29 46% 25 28% 29 72% 73 Finland 22% 14 78% 50 52% 70 48% 64 33% 122 67% 246 France 39% 197 61% 306 40% 86 60% 128 28% 316 72% 832 Gabon 0% 0 0% 0 33% 2 67% 4 55% 6 45% 5 Gambia 0% 0 100% 8 29% 9 71% 22 14% 6 86% 38 Georgia 75% 452 25% 147 51% 28 49% 27 31% 179 69% 406 Ghana 51% 179 49% 172 30% 92 70% 214 15% 147 85% 811 Greenland 2% 1 98% 53 46% 28 54% 33 41% 39 59% 55 Grenada 67% 10 33% 5 60% 6 40% 4 37% 28 63% 48 Guatemala 46% 75 54% 89 35% 56 65% 104 24% 77 76% 241 Guinea 0% 0 0% 0 7% 1 93% 14 22% 5 78% 18 Guyana 0% 0 100% 14 36% 4 64% 6% 3 94% 44 Haiti 22% 12 78% 43 18% 12 82% 54 16% 49 84% 256 Hong Kong SAR PRC 56% 22 44% 17 47% 44 53% 49 25% 98 75% 295 Iceland 30% 15 70% 35 33% 20 67% 40 34% 36 66% 71 India 48% 105 52% 115 15% 12 85% 70 14% 133 86% 800 Indonesia 52% 23 48% 21 31% 11 69% 25 15% 30 85% 169 Irag 50% 1 50% 1 25% 1 75% 50% 4 50% 4 Ireland 39% 30 61% 46 36% 45 64% 80 28% 75 72% 196 Israel 54% 45 46% 38 25% 21 75% 63 13% 37 88% 259 Italy 43% 42 57% 55 49% 84 51% 89 24% 127 76% 399 Jamaica 79% 69 21% 18 67% 26 33% 13 33% 72 67% 145 Japan 46% 56 54% 67 27% 18 73% 49 20% 55 80% 221 Jordan 60% 109 40% 74 57% 49 43% 37 16% 74 84% 381 Kenya 67% 14 33% 7 23% 37 77% 125 19% 44 81% 192 Kyrgyzstan 66% 49 34% 25 54% 27 46% 23 20% 35 80% 143 Lebanon 83% 30 17% 6 59% 17 41% 12 16% 25 84% 134 Luxembourg 33% 15 67% 31 26% 11 74% 31 20% 42 80% 163 Macao 34% 16 66% 31 54% 33 46% 28 29% 58 71% 143 Malawi 47% 45 53% 50 34% 32 66% 63 27% 72 73% 199 Malaysia 42% 98 58% 138 53% 35 47% 31 15% 87 85% 477 Mali 74% 17 26% 6 26% 17 74% 49 11% 33 89% 269 Malta 44% 15 56% 19 25% 37 75% 113 28% 160 72% 408 Mexico 48% 194 52% 213 44% 120 56% 151 31% 294 69% 658 Moldova 52% 106 48% 97 55% 22 45% 18 35% 130 65% 238 Mongolia 36% 31 64% 55 67% 57 33% 28 25% 67 75% 202 Morocco 28% 29 72% 76 43% 30 57% 40 17% 48 83% 227 GMMP2020 80 Who Makes the News? PRESENTER REPORTER SUBJECTS & SOURCES APPENDIX 5-2 Female % N Male % N Female % N Male % N Female % N Male % N Myanmar 81% 48 19% 11 24% 12 76% 38 15% 20 85% 113 Namibia 0% 0 100% 1 33% 13 68% 27 36% 30 64% 54 Nepal 38% 59 62% 96 22% 23 78% 81 23% 194 77% 636 Netherlands 0% 0 100% 5 29% 23 71% 55 29% 115 71% 280 New Zealand 84% 31 16% 6 60% 44 40% 29 33% 72 67% 144 Nicaragua 41% 22 59% 32 56% 23 44% 18 31% 29 69% 64 Niger 0% 0 0% 0 37% 13 63% 22 38% 11 62% 18 Nigeria 54% 55 46% 46 15% 20 85% 115 14% 64 86% 396 Norway 66% 71 34% 37 42% 74 58% 104 33% 150 67% 307 Pakistan 58% 105 42% 75 7% 5 93% 65 17% 122 83% 583 Palestine 7% 4 93% 53 29% 10 71% 25 16% 34 84% 185 Papua New Guinea 100% 15 0% 0 44% 18 56% 23 16% 10 84% 54 Paraguay 51% 54 49% 51 16% 7 84% 36 19% 53 81% 231 Peru 60% 170 40% 115 48% 89 52% 96 32% 204 68% 427 Poland 44% 122 56% 158 37% 75 63% 126 27% 234 73% 639 Portugal 27% 35 73% 96 57% 77 43% 59 34% 126 66% 243 Puerto Rico 41% 28 59% 40 57% 33 43% 25 35% 97 65% 177 Romania 62% 117 38% 71 56% 118 44% 91 34% 257 66% 491 Russian Federation 0% 0 0% 0 61% 48 39% 31 26% 60 74% 173 Senegal 0% 0 100% 7 23% 9 77% 30 17% 21 83% 105 Serbia 64% 96 36% 55 64% 47 36% 27 19% 80 81% 348 Seychelles 100% 5 0% 0 71% 10 29% 4 26% 9 74% 25 Sierra Leone 75% 3 25% 1 50% 2 50% _L 73% 11 27% 4 South Africa 62% 28 38% 17 58% 57 42% Al 37% 89 63% 154 South Sudan 61% 17 39% 11 4% 1 96% 24 18% 16 82% 75 Spain 70% 234 30% 99 55% 166 45% 136 31% 353 69% 791 Suriname 44% 28 56% 36 10% 2 90% 18 36% 30 64% 53 Sweden 77% 60 23% 18 44% 92 56% 115 38% 234 63% 390 Switzerland 55% 65 45% 53 37% 102 63% 171 28% 274 72% 701 Taiwan Province of China 81% 167 19% 38 53% 229 47% 202 29% 216 71% 526 Tanzania 59% 58 41% 41 44% 46 56% 58 30% 100 70% 238 Togo 38% 19 62% 31 20% 7 80% 28 33% 60 67% 120 Trinidad and Tobago 100% 22 0% 0 72% 23 28% 9 28% 37 72% 94 Tunisia 58% 97 42% 71 53% 51 47% 45 21% 98 79% 367 Turkey 44% 246 56% 307 16% 66 84% 353 22% 270 78% 980 Uganda 63% 45 37% 26 31% 22 69% 48 24% 132 76% 408 United Kingdom 54% 91 46% 76 39% 149 61% 234 32% 310 68% 667 United States of America 56% 37 44% 29 45% 85 55% 103 34% 288 66% 548 Uruguay 29% 107 71% 257 17% 26 83% 129 24% 224 76% 721 Venezuela 55% 102 45% 85 44% 35 56% 45 18% 64 82% 285 Vietnam 28% 7 72% 18 27% 8 73% 22 30% 49 70% 112 Zambia 75% 3 25% 1 40% 6 60% 9 84% 21 16% 4 Zimbabwe 35% 7 65% 13 22% 17 78% 60 25% 77 75% 237 GMMP2020 81 Who Makes the News? 3. Subjects & sources in newspaper, television and radio news PRINT RADIO TELEVISION APPENDIX 5-3 Female % N Male % N Female % N Male % N Female % N Male % N Antigua and Barbuda 0% 0 100% 6 14% 2 86% 12 67% 2 33% 1 Argentina 18% 69 82% 305 19% 32 81% 135 28% 27 72% 70 Australia 36% 239 64% 419 19% 17 81% 71 27% 115 73% 315 Austria 28% 18 72% 47 12% 2 88% 15 26% 6 74% 17 Bangladesh 15% 68 85% 383 32% 6 68% 13 19% 26 81% 114 Belgium 28% 59 72% 153 20% 11 80% 45 26% 43 74% 123 Benin 25% 19 75% 56 35% 17 65% 31 19% 4 81% 17 Bolivia 19% 30 81% 132 21% 26 79% 97 27% 123 73% 329 Bosnia and Herzegovina 29% 32 71% 80 21% 25 79% 95 13% 24 87% 159 Botswana 31% 8 69% 18 24% 6 76% 19 0% 0 100% 1 Brazil 27% 81 73% 217 26% 35 74% 102 28% 81 72% 207 Bulgaria 36% 10 64% 18 60% 3 40% 2 27% 14 73% 37 Burkina Faso 21% 34 79% 125 15% 15 85% 83 10% 8 90% 76 Cambodia 31% 20 69% 44 33% 2 67% 4 56% 5 44% 4 Cameroon 20% 20 80% 79 30% 12 70% 28 14% 17 86% 104 Canada 30% 109 70% 251 19% 30 81% 125 41% 74 59% 106 Cayman Islands 42% 20 58% 28 21% 3 79% 11 100% 3 0% 0 Central African Republic 0% 0 100% 16 24% 4 76% 13 0% 0 0% 0 Chad 11% 1 89% 8 17% 1 83% 5 41% 9 59% 13 Chile 34% 77 66% 150 17% 39 83% 192 26% 105 74% 299 People's Republic of China 26% 16 74% 45 25% 16 75% 48 29% 53 71% 132 Colombia 25% 29 75% 87 20% 28 80% 109 0% 0 0% 0 Congo 100% 4 0% 0 100% 3 0% 0 100% 1 0% 0 Congo (Democratic Republic of the) 15% 3 85% 17 21% 31 79% 116 15% 2 85% 11 Costa Rica 27% 57 73% 151 31% 27 69% 60 33% 56 67% 114 Cuba 19% 12 81% 50 36% 25 64% 44 13% 13 87% 89 Cyprus 23% 36 77% 120 3% 1 97% 31 21% 55 79% 203 Denmark 33% 57 67% 114 39% 27 61% 43 35% 24 65% 44 Dominica 57% 4 43% 3 18% 2 82% 9 0% 0 0% 0 Dominican Republic 21% 28 79% 104 0% 0 100% 12 42% 16 58% 22 Ecuador 26% 43 74% 125 22% 15 78% 52 23% 67 77% 226 Egypt 15% 18 85% 105 9% 5 91% 48 10% 4 90% 37 El Salvador 20% 13 80% 51 24% 14 76% 44 28% 24 72% 62 Estonia 14% 8 86% 48 26% 7 74% 20 39% 16 61% 25 Eswatini 27% 23 73% 61 25% 1 75% 3 0% 0 0% 0 Ethiopia 18% 7 83% 33 0% 0 100% 9 10% 9 90% 82 Fiji 39% 20 61% 31 19% 6 81% 25 15% 3 85% 17 Finland 31% 86 69% 195 49% 17 51% 18 37% 19 63% 33 France 29% 113 71% 277 25% 146 75% 435 32% 57 68% 120 Gabon 55% 6 45% 5 0% 0 0% 0 0% 0 0% 0 Gambia 12% 3 88% 23 20% 1 80% 4 15% 2 85% 11 Georgia 15% 9 85% 50 31% 63 69% 142 33% 107 67% 214 Ghana 15% 45 85% 258 9% 28 91% 267 21% 74 79% 286 GMMP2020 82 Who Makes the News? PRINT RADIO TELEVISION APPENDIX 5-3 Female % N Male % N Female % N Male % N Female % N Male % N Greenland 47% 26 53% 29 32% 9 68% 19 36% 4 64% 7 Grenada 33% 8 67% 16 15% 2 85% 11 46% 18 54% 21 Guatemala 24% 28 76% 91 24% 35 76% 108 25% 14 75% 42 Guinea 22% 2 78% 7 0% 0 0% 0 21% 3 79% 11 Guyana 7% 2 93% 26 20% 1 80% _4_ 0% 0 100% 14 Haiti 13% 1 88% 7 14% 34 86% 216 30% 14 70% 33 Hong Kong SAR PRC 17% 47 83% 235 45% 31 55% 38 48% 20 52% 22 Ice land 40% 12 60% 18 22% 8 78% 28 39% 16 61% 25 India 15% 88 85% 505 5% 3 95% 61 15% 42 85% 234 Indonesia 16% 25 84% 135 0% 0 0% 0 13% 5 87% 34 Irag 0% 0 0% 0 0% 0 0% 0 50% 4 50% 4 Ireland 29% 46 71% 115 26% 11 74% 31 26% 18 74% 50 Israel 16% 25 84% 127 5% 1 95% 18 9% 11 91% 114 Italy 24% 81 76% 262 32% 23 68% 49 21% 23 79% 88 Jamaica 44% 38 56% 49 33% 18 67% 37 21% 16 79% 59 Japan 18% 20 82% 90 0% 0 0% 0 21% 35 79% 131 Jordan 32% 61 68% 130 8% 4 92% 49 4% 9 96% 202 Kenya 19% 37 81% 154 11% 2 89% 16 19% 5 81% 22 Kyrgyzstan 11% 8 89% 65 22% 5 78% 18 27% 22 73% 60 Lebanon 7% 3 93% 42 19% 6 81% 26 20% 16 80% 66 Luxembourg 20% 24 80% 98 21% 11 79% 41 23% 7 77% 24 Macao 24% 30 76% 96 29% 10 71% 24 44% 18 56% 23 Malawi 32% 41 68% 87 15% 11 85% 62 29% 20 71% 50 Malaysia 19% 51 81% 214 6% 2 94% 29 13% 34 87% 234 Mali 10% 20 90% 179 12% 8 88% 60 14% 5 86% 30 Malta 29% 120 71% 297 31% 11 69% 24 25% 29 75% 87 Mexico 28% 81 72% 205 31% 121 69% 273 34% 92 66% 180 Moldova 43% 15 57% 20 29% 20 71% 50 36% 95 64% 168 Mongolia 21% 24 79% 88 28% 11 72% 28 27% 32 73% 86 Morocco 16% 20 84% 104 13% 7 87% 48 22% 21 78% 75 Myanmar 18% 7 83% 33 21% 8 79% 30 9% 5 91% 50 Namibia 37% 30 63% 51 0% 0 0% 0 0% 0 100% 3 Nepal 27% 155 73% 425 12% 12 88% 89 18% 27 82% 122 Netherlands 30% 99 70% 232 14% 2 86% 12 28% 14 72% 36 New Zealand 42% 47 58% 66 18% 4 82% 18 26% 21 74% 60 Nicaragua 9% 1 91% 10 27% 11 73% 30 41% 17 59% 24 Niger 33% 1 67% 2 56% 5 44% 4 29% 5 71% 12 Nigeria 11% 25 89% 199 11% 7 89% 57 19% 32 81% 140 Norway 28% 69 72% 177 36% 28 64% 49 40% 53 60% 81 Pakistan 17% 67 83% 317 7% 1 93% 14 18% 54 82% 252 Palestine 11% 11 89% 90 24% 13 76% 41 16% 10 84% 54 Papua New Guinea 13% 2 87% 13 23% 3 77% 10 14% 5 86% 31 Paraguay 14% 19 86% 120 24% 16 76% 52 23% 18 77% 59 Peru 23% 36 77% 123 23% 24 77% 81 39% 144 61% 223 Poland 22% 29 78% 100 39% 50 61% 79 25% 155 75% 460 Portugal 36% 44 64% 77 31% 11 69% 25 33% 71 67% 141 GMMP2020 83 Who Makes the News? PRINT RADIO TELEVISION APPENDIX 5-3 Female % N Male % N Female % N Male % N Female % N Male % N Puerto Rico 35% 64 65% 121 36% 9 64% 16 38% 24 63% 40 Romania 26% 76 74% 217 36% 45 64% 79 41% 136 59% 195 Russian Federation 26% 60 74% 173 0% 0 0% 0 0% 0 0% 0 Senegal 13% 7 87% 45 7% 2 93% 25 26% 12 74% 35 Serbia 17% 30 83% 150 24% 15 76% 48 19% 35 81% 150 Seychelles 21% 5 79% 19 0% 0 0% 0 40% 4 60% 6 Sierra Leone 83% 5 17% 1 75% 6 25% 2 0% 0 100% 1 South Africa 34% 50 66% 95 74% 17 26% 6 29% 22 71% 53 South Sudan 23% 10 77% 33 9% 2 91% 21 16% 4 84% 21 Spain 21% 71 79% 274 32% 77 68% 160 36% 205 64% 357 Suriname 0% 0 100% 3 25% 6 75% 18 43% 24 57% 32 Sweden 37% 187 63% 315 38% 15 62% 24 39% 32 61% 51 Switzerland 28% 213 72% 543 27% 21 73% 57 28% 40 72% 101 Taiwan Province of China 16% 28 84% 145 32% 7 68% 15 33% 181 67% 366 Tanzania 26% 36 74% 101 37% 22 63% 38 30% 42 70% 99 Togo 31% 17 69% 37 25% 18 75% 53 45% 25 55% 30 Trinidad and Tobago 27% 28 73% 77 50% 4 50% 4 28% 5 72% 13 Tunisia 21% 35 79% 128 29% 38 71% 95 15% 25 85% 144 Turkey 26% 68 74% 196 16% 47 84% 238 22% 155 78% 546 Uganda 24% 69 76% 216 26% 24 74% 69 24% 39 76% 123 United Kingdom 32% 197 68% 413 21% 9 79% 34 32% 104 68% 220 United States of America 35% 260 65% 487 32% 8 68% 17 31% 20 69% 44 Uruguay 31% 101 69% 226 17% 58 83% 288 24% 65 76% 207 Venezuela 22% 13 78% 46 18% 15 82% 69 17% 36 83% 170 Vietnam 39% 36 61% 57 23% 8 77% 27 15% 5 85% 28 Zambia 92% 11 8% 1 67% 4 33% 2 86% 6 14% 1 Zimbabwe 22% 60 78% 209 43% 3 57% 4 37% 14 63% 24 GMMP2020 84 Who Makes the News? 4. Subjects & sources in newspaper, television and radio news, by major topic areas APPENDIX 5-4 POLITICS AND GOVERNMENT ECONOMY SCIENCE AND HEALTH SOCIAL AND LEGAL CRIME AND VIOLENCE GENDER & RELATED CELEBRITY, ARTS AND MEDIA, SPORTS OTHER Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N Antigua and Barbuda 67% 2 33% 1 0% 0 100% 4 0% 0 100% 3 0% 0 100% 10 0% 0 0% 0 100% 1 0% 0 50% 1 50% 1 0% 0 0% 0 Argentina 15% 21 85% 117 16% 25 84% 132 18% 9 82% 42 25% 38 75% 115 21% 20 79% 74 56% 5 44% 4 28% 10 72% 26 0% 0 0% 0 Australia 32% 69 68% 144 24% 55 76% 173 32% 30 68% 63 46% 112 54% 129 35% 42 65% 79 58% 7 42% 5 21% 54 79% 205 22% 2 78% 7 Austria 21% 5 79% 19 28% 10 72% 26 40% 4 60% 6 17% 2 83% 10 24% 4 76% 13 0% 0 0% 0 33% 1 67% 2 0% 0 100% 3 Bangladesh 23% 19 77% 63 21% 14 79% 52 25% 18 75% 53 9% 9 91% 94 11% 15 89% 123 14% 15 86% 94 28% 10 72% 26 0% 0 100% 5 Belgium 23% 18 77% 60 39% 7 61% 11 33% 12 67% 24 47% 14 53% 16 32% 14 68% 30 71% 5 29% 2 23% 8 77% 27 0% 0 100% 4 Benin 50% 1 50% 1 57% 4 43% 3 0% 0 100% 4 50% 2 50% 2 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 26% 33 74% 94 Bolivia 18% 48 82% 212 27% 31 73% 83 31% 34 69% 76 22% 19 78% 66 24% 15 76% 47 39% 16 61% 25 20% 11 80% 43 45% 5 55% 6 Bosnia and Herzegovina 14% 21 86% 132 24% 16 76% 52 42% 30 58% 41 8% 1 92% 12 8% 6 92% 66 0% 0 0% 0 18% 7 82% 31 0% 0 0% 0 Botswana 16% 3 84% 16 20% 2 80% 8 67% 2 33% 1 50% 4 50% 4 17% 1 83% 5 100% 2 0% 0 0% 0 100% 2 0% 0 100% 2 Brazil 20% 40 80% 158 16% 23 84% 124 35% 38 65% 71 48% 54 52% 59 23% 24 77% 80 47% 8 53% 9 19% 5 81% 21 56% 5 44% 4 Bulgaria 28% 7 72% 18 33% 3 67% 6 9% 1 91% 10 41% 7 59% 10 33% 5 67% 10 67% 2 33% 1 50% 2 50% 2 0% 0 0% 0 Burkina Faso 10% 11 90% 104 5% 2 95% 38 27% 17 73% 45 24% 19 76% 59 0% 0 100% 1 100% 4 0% 0 10% 4 90% 37 0% 0 0% 0 Cambodia 24% 4 76% 13 17% 1 83% 5 48% 10 52% 11 47% 9 53% 10 13% 2 87% 13 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 Cameroon 14% 7 86% 44 21% 13 79% 48 44% 8 56% 10 19% 15 81% 62 67% 2 33% 1 0% 0 0% 0 9% 3 91% 29 6% 1 94% 17 Canada 19% 37 81% 156 38% 29 62% 48 41% 81 59% 116 47% 34 53% 38 25% 13 75% 38 0% 0 0% 0 18% 18 82% 82 20% 1 80% 4 Cayman Islands 50% 1 50% 1 36% 4 64% 7 56% 10 44% 8 50% 8 50% 8 10% 1 90% 9 0% 0 0% 0 33% 2 67% 4 0% 0 100% 2 Central African Republic 13% 4 87% 26 0% 0 0% 0 0% 0 100% 1 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 Chad 18% 2 82% 9 29% 2 71% 5 57% 4 43% 3 43% 3 57% 4 0% 0 100% 1 0% 0 100% 2 0% 0 100% 2 0% 0 0% 0 Chile 23% 30 77% 99 37% 63 63% 109 29% 27 71% 65 33% 65 68% 135 10% 13 90% 119 50% 2 50% 2 16% 21 84% 112 0% 0 0% 0 People's Republic of China 10% 5 90% 46 23% 21 77% 71 40% 19 60% 29 30% 25 70% 58 10% 1 90% 9 0% 0 0% 0 54% 14 46% 12 0% 0 0% 0 Colombia 14% 9 86% 55 25% 10 75% 30 15% 5 85% 28 26% 10 74% 28 26% 9 74% 25 88% 7 13% 1 19% 7 81% 29 0% 0 0% 0 Congo 100% 3 0% 0 0% 0 0% 0 100% 1 0% 0 100% 3 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 Congo (Democratic Republic of the) 17% 5 83% 25 33% 1 67% 2 11% 1 89% 8 23% 3 77% 10 0% 0 100% 2 0% 0 0% 0 0% 0 100% 2 21% 26 79% 95 Costa Rica 24% 15 76% 48 29% 31 71% 76 35% 19 65% 35 32% 30 68% 64 30% 29 70% 69 43% 15 57% 20 0% 0 100% 9 20% 1 80% 4 Cuba 22% 15 78% 54 15% 5 85% 29 30% 17 70% 39 42% 5 58% 7 100% 1 0% 0 0% 0 0% 0 11% 7 89% 54 0% 0 0% 0 Cyprus 11% 22 89% 176 13% 3 88% 21 32% 26 68% 56 50% 7 50% 7 20% 18 80% 74 75% 3 25% 1 42% 11 58% 15 33% 2 67% 4 Denmark 36% 32 64% 57 41% 19 59% 27 32% 20 68% 43 38% 18 63% 30 27% 13 73% 35 56% 5 44% 4 17% 1 83% 5 0% 0 0% 0 Dominica 50% 2 50% 2 17% 1 83% 5 0% 0 100% 1 50% 2 50% 2 0% 0 100% 1 0% 0 0% 0 50% 1 50% 1 0% 0 0% 0 Dominican Republic 3% 1 97% 28 18% 12 82% 53 21% 3 79% 11 38% 18 62% 29 40% 4 60% 6 50% 1 50% 1 30% 3 70% 7 40% 2 60% 3 Ecuador 17% 13 83% 64 20% 19 80% 74 44% 22 56% 28 44% 11 56% 14 23% 48 77% 160 50% 3 50% 3 13% 9 87% 60 0% 0 0% 0 Egypt 15% 10 85% 56 4% 1 96% 22 2% 1 98% 51 30% 13 70% 30 9% 2 91% 21 0% 0 0% 0 0% 0 100% 10 0% 0 0% 0 El Salvador 23% 8 77% 27 29% 19 71% 47 23% 3 77% 10 37% 11 63% 19 14% 5 86% 31 21% 5 79% 19 0% 0 100% 4 0% 0 0% 0 Estonia 25% 3 75% 9 29% 12 71% 29 45% 5 55% 6 7% 2 93% 25 18% 2 82% 9 0% 0 0% 0 32% 7 68% 15 0% 0 0% 0 Eswatini 50% 5 50% 5 17% 2 83% 10 100% 2 0% 0 23% 7 77% 24 33% 5 67% 10 0% 0 0% 0 17% 3 83% 15 0% 0 0% 0 Ethiopia 10% 3 90% 27 11% 4 89% 31 6% 1 94% 17 22% 6 78% 21 0% 0 100% 4 0% 0 0% 0 0% 0 100% 12 14% 2 86% 12 Fiji 13% 2 88% 14 33% 6 67% 12 43% 3 57% 4 33% 13 68% 27 33% 2 67% 4 0% 0 0% 0 30% 3 70% 7 0% 0 100% 5 Finland 32% 36 68% 76 38% 23 62% 37 38% 20 62% 33 35% 22 65% 40 20% 10 80% 41 0% 0 0% 0 37% 11 63% 19 0% 0 0% 0 France 22% 69 78% 251 20% 42 80% 171 37% 45 63% 76 32% 48 68% 101 41% 54 59% 78 50% 13 50% 13 23% 40 77% 137 50% 5 50% 5 85 APPENDIX 5-4 POLITICS AND GOVERNMENT ECONOMY SCIENCE AND HEALTH SOCIAL AND LEGAL CRIME AND VIOLENCE GENDER & RELATED CELEBRITY, ARTS AND MEDIA, SPORTS OTHER Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N Gabon 100% 3 0% 0 50% 1 50% 1 0% 0 100% 1 50% 2 50% 2 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Gambia 17% 3 83% 15 14% 1 86% 6 33% 1 67% 2 9% 1 91% 10 0% 0 100% 2 0% 0 0% 0 0% 0 100% 1 0% 0 100% 2 Georgia 27% 87 73% 239 15% 2 85% 11 56% 35 44% 28 33% 40 67% 81 24% 14 76% 44 0% 0 0% 0 25% 1 75% 3 0% 0 0% 0 Ghana 15% 54 85% 297 9% 13 91% 129 29% 35 71% 86 14% 36 86% 224 5% 1 95% 19 0% 0 0% 0 13% 8 88% 56 0% 0 0% 0 Greenland 36% 9 64% 16 19% 3 81% 13 46% 11 54% 13 73% 8 27% 3 0% 0 100% 6 50% 4 50% 4 100% 3 0% 0 100% 1 0% 0 Grenada 35% 6 65% 11 42% 10 58% 14 0% 0 100% 3 39% 9 61% 14 33% 3 67% 6 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Guatemala 17% 16 83% 76 19% 6 81% 26 22% 7 78% 25 21% 12 79% 45 34% 30 66% 58 83% 5 17% 1 9% 1 91% 10 0% 0 0% 0 Guinea 14% 2 86% 12 0% 0 100% 5 0% 0 0% 0 75% 3 25% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Guyana 9% 1 91% 10 0% 0 100% 17 0% 0 100% 2 14% 1 86% 6 10% 1 90% 9 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Haiti 9% 12 91% 122 23% 8 77% 27 14% 3 86% 19 17% 7 83% 35 24% 14 76% 44 100% 1 0% 0 33% 4 67% 8 0% 0 100% 1 Hong Kong SAR PRC 22% 39 78% 135 23% 16 77% 54 27% 7 73% 19 29% 21 71% 51 26% 10 74% 29 0% 0 0% 0 42% 5 58% 7 0% 0 0% 0 Iceland 11% 2 89% 16 47% 17 53% 19 41% 7 59% 10 50% 7 50% 7 0% 0 100% 8 100% 1 0% 0 15% 2 85% 11 0% 0 0% 0 India 11% 36 89% 295 12% 17 88% 124 14% 12 86% 71 17% 23 83% 113 15% 24 85% 137 32% 9 68% 19 25% 7 75% 21 20% 5 80% 20 Indonesia 3% 1 97% 28 10% 2 90% 19 16% 3 84% 16 16% 15 84% 81 23% 6 77% 20 0% 0 100% 2 67% 2 33% 1 33% 1 67% 2 Irag 0% 0 100% 4 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 100% 3 0% 0 0% 0 0% 0 Ireland 24% 15 76% 47 21% 11 79% 41 20% 12 80% 49 32% 10 68% 21 27% 7 73% 19 0% 0 0% 0 49% 18 51% 19 100% 2 0% 0 Israel 12% 12 88% 92 0% 0 100% 3 9% 9 91% 94 11% 5 89% 41 21% 6 79% 22 0% 0 0% 0 0% 0 0% 0 42% 5 58% 7 Italy 25% 26 75% 79 14% 13 86% 83 11% 8 89% 63 40% 33 60% 49 31% 35 69% 78 0% 0 0% 0 21% 12 79% 45 0% 0 100% 2 Jamaica 23% 12 77% 41 31% 11 69% 24 28% 11 72% 28 57% 31 43% 23 19% 5 81% 21 0% 0 0% 0 20% 2 80% 8 0% 0 0% 0 Japan 8% 7 92% 79 18% 15 82% 69 47% 7 53% 8 41% 7 59% 10 27% 12 73% 33 75% 3 25% 1 16% 4 84% 21 0% 0 0% 0 Jordan 16% 24 84% 128 26% 9 74% 26 11% 12 89% 95 24% 15 76% 47 6% 3 94% 51 0% 0 0% 0 24% 11 76% 34 0% 0 0% 0 Kenya 16% 14 84% 71 21% 9 79% 34 27% 3 73% 8 25% 15 75% 44 4% 1 96% 24 0% 0 0% 0 0% 0 100% 7 33% 2 67% 4 Kyrgyzstan 11% 10 89% 79 10% 3 90% 26 43% 15 57% 20 29% 2 71% 5 24% 4 76% 13 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 Lebanon 11% 7 89% 59 5% 1 95% 20 20% 3 80% 12 28% 9 72% 23 12% 2 88% 15 0% 0 0% 0 38% 3 63% 5 0% 0 0% 0 Luxembourg 20% 19 80% 77 20% 2 80% 8 27% 8 73% 22 27% 4 73% 11 12% 4 88% 30 0% 0 100% 1 22% 4 78% 14 100% 1 0% 0 Macao 19% 7 81% 30 19% 6 81% 25 40% 21 60% 31 33% 12 67% 24 30% 8 70% 19 0% 0 0% 0 22% 4 78% 14 0% 0 0% 0 Malawi 0% 0 100% 4 35% 6 65% 11 0% 0 100% 5 39% 7 61% 11 50% 5 50% 5 0% 0 100% 1 16% 4 84% 21 26% 50 74% 141 Malaysia 3% 4 97% 150 20% 15 80% 61 12% 8 88% 57 24% 12 76% 37 27% 35 73% 95 0% 0 0% 0 11% 8 89% 67 33% 5 67% 10 Mali 6% 12 94% 187 12% 3 88% 22 20% 2 80% 8 19% 6 81% 26 33% 7 67% 14 0% 0 0% 0 17% 1 83% 5 22% 2 78% 7 Malta 14% 13 86% 82 45% 15 55% 18 66% 23 34% 12 39% 45 61% 71 23% 18 78% 62 100% 1 0% 0 21% 41 79% 157 40% 4 60% 6 Mexico 13% 26 87% 167 18% 14 82% 63 15% 12 85% 69 46% 132 54% 156 23% 24 77% 79 64% 43 36% 24 29% 41 71% 100 100% 2 0% 0 Moldova 29% 34 71% 82 34% 11 66% 21 46% 31 54% 36 47% 22 53% 25 25% 7 75% 21 0% 0 0% 0 40% 4 60% 6 31% 21 69% 47 Mongolia 17% 10 83% 50 24% 11 76% 34 54% 26 46% 22 19% 16 81% 68 20% 3 80% 12 0% 0 0% 0 6% 1 94% 16 0% 0 0% 0 Morocco 6% 6 94% 90 6% 2 94% 33 26% 12 74% 35 21% 7 79% 26 13% 4 87% 27 0% 0 0% 0 52% 16 48% 15 50% 1 50% 1 Myanmar 0% 0 100% 8 17% 4 83% 19 18% 14 82% 63 17% 2 83% 10 0% 0 100% 12 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 Namibia 54% 7 46% 6 20% 4 80% 16 33% 1 67% 2 43% 9 57% 12 33% 3 67% 6 0% 0 0% 0 33% 6 67% 12 0% 0 0% 0 Nepal 10% 17 90% 145 26% 42 74% 120 10% 12 90% 103 33% 75 67% 152 28% 17 72% 43 43% 19 57% 25 19% 9 81% 38 23% 3 77% 10 Netherlands 26% 34 74% 96 31% 24 69% 53 25% 8 75% 24 28% 9 72% 23 19% 7 81% 30 0% 0 0% 0 39% 33 61% 52 0% 0 100% 2 New Zealand 40% 21 60% 31 47% 16 53% 18 41% 12 59% 17 29% 13 71% 32 9% 2 91% 20 0% 0 0% 0 24% 8 76% 26 0% 0 0% 0 Nicaragua 0% 0 100% 2 100% 2 0% 0 67% 2 33% 1 0% 0 100% 3 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 30% 25 70% 58 Niger 33% 5 67% 10 25% 1 75% 3 100% 1 0% 0 43% 3 57% 4 0% 0 0% 0 100% 1 0% 0 0% 0 100% 1 0% 0 0% 0 GMMP2020 86 Who Makes Lhe News? (p^ APPENDIX 5-4 POLITICS AND GOVERNMENT ECONOMY SCIENCE AND HEALTH SOCIAL AND LEGAL CRIME AND VIOLENCE GENDER & RELATED CELEBRITY, ARTS AND MEDIA, SPORTS OTHER Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N Nigeria 16% 18 84% 95 9% 13 91% 134 29% 6 71% 15 13% 11 87% 71 15% 11 85% 60 50% 3 50% 3 10% 2 90% 18 0% 0 0% 0 Norway 31% 30 69% 67 29% 35 71% 85 39% 41 61% 64 58% 21 42% 15 24% 11 76% 35 0% 0 0% 0 24% 12 76% 39 0% 0 100% 2 Pakistan 13% 49 87% 329 3% 1 97% 35 9% 2 91% 20 13% 10 87% 68 28% 29 72% 76 45% 29 55% 36 10% 2 90% 19 0% 0 0% 0 Palestine 9% 5 91% 48 8% 4 92% 45 18% 4 82% 18 22% 2 78% 7 20% 16 80% 66 75% 3 25% 1 0% 0 0% 0 0% 0 0% 0 Papua New Guinea 21% 3 79% 11 17% 1 83% 5 14% 2 86% 12 13% 3 88% 21 0% 0 100% 4 0% 0 0% 0 0% 0 100% 1 100% 1 0% 0 Paraguay 11% 5 89% 41 21% 11 79% 41 15% 9 85% 52 20% 10 80% 40 21% 10 79% 38 67% 2 33% 1 25% 3 75% 9 25% 3 75% 9 Peru 8% 6 92% 69 29% 27 71% 65 30% 25 70% 57 44% 29 56% 37 32% 62 68% 129 54% 13 46% 11 42% 42 58% 59 0% 0 0% 0 Poland 14% 37 86% 232 20% 18 80% 71 32% 49 68% 102 28% 40 72% 101 37% 74 63% 126 0% 0 100% 4 84% 16 16% 3 0% 0 0% 0 Portugal 27% 20 73% 54 40% 27 60% 41 32% 31 68% 65 52% 27 48% 25 41% 17 59% 24 0% 0 0% 0 11% 4 89% 34 0% 0 0% 0 Puerto Rico 32% 27 68% 57 25% 2 75% 6 21% 9 79% 33 65% 22 35% 12 47% 9 53% 10 65% 15 35% 8 20% 12 80% 47 20% 1 80% 4 Romania 26% 97 74% 276 37% 16 63% 27 46% 29 54% 34 43% 37 57% 49 26% 8 74% 23 60% 3 40% 2 46% 67 54% 80 0% 0 0% 0 Russian Federation 12% 9 88% 67 23% 10 77% 34 47% 8 53% 9 31% 19 69% 42 42% 5 58% 7 0% 0 0% 0 39% 9 61% 14 0% 0 0% 0 Senegal 14% 3 86% 18 16% 3 84% 16 22% 2 78% 7 18% 11 82% 50 10% 1 90% 9 0% 0 0% 0 0% 0 0% 0 17% 1 83% 5 Serbia 16% 15 84% 80 28% 13 72% 34 34% 18 66% 35 13% 7 87% 46 9% 12 91% 115 67% 2 33% 1 26% 13 74% 37 0% 0 0% 0 Seychelles 14% 2 86% 12 50% 2 50% 2 0% 0 0% 0 40% 2 60% 3 0% 0 100% 1 0% 0 0% 0 0% 0 100% 4 50% 3 50% 3 Sierra Leone 86% 6 14% 1 50% 3 50% 3 0% 0 0% 0 100% 2 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 South Africa 46% 11 54% 13 42% 19 58% 26 35% 6 65% 11 31% 11 69% 25 43% 24 57% 32 61% 11 39% 7 17% 5 83% 25 12% 2 88% 15 South Sudan 10% 3 90% 26 27% 8 73% 22 14% 1 86% 6 17% 3 83% 15 14% 1 86% 6 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Spain 20% 50 80% 194 43% 61 57% 80 42% 93 58% 129 37% 32 63% 54 26% 32 74% 89 82% 28 18% 6 7% 8 93% 100 26% 49 74% 139 Suriname 38% 9 63% 15 34% 10 66% 19 25% 1 75% 3 53% 8 47% 7 0% 0 100% 6 0% 0 0% 0 40% 2 60% 3 0% 0 0% 0 Sweden 34% 30 66% 57 47% 60 53% 67 44% 34 56% 44 30% 50 70% 116 24% 19 76% 59 80% 4 20% 1 49% 37 51% 39 0% 0 100% 7 Switzerland 24% 85 76% 276 22% 36 78% 126 25% 21 75% 63 37% 50 63% 85 26% 11 74% 31 75% 3 25% 1 36% 66 64% 119 100% 2 0% 0 Taiwan Province of China 28% 83 72% 217 32% 44 68% 95 25% 18 75% 53 32% 26 68% 56 26% 23 74% 67 25% 1 75% 3 39% 21 61% 33 0% 0 100% 2 Tanzania 21% 16 79% 59 40% 31 60% 46 38% 9 63% 15 30% 26 70% 60 17% 1 83% 5 0% 0 0% 0 24% 8 76% 26 25% 9 75% 27 Togo 46% 33 54% 38 23% 5 77% 17 21% 4 79% 15 10% 2 90% 19 0% 0 0% 0 0% 0 0% 0 0% 0 100% 6 39% 16 61% 25 Trinidad and Tobago 36% 5 64% 9 13% 2 88% 14 43% 9 57% 12 30% 3 70% 7 31% 11 69% 25 0% 0 0% 0 20% 6 80% 24 25% 1 75% 3 Tunisia 9% 8 91% 86 19% 8 81% 34 26% 28 74% 81 25% 25 75% 74 39% 20 61% 31 0% 0 0% 0 13% 9 87% 61 0% 0 0% 0 Turkey 10% 16 90% 152 19% 18 81% 78 29% 52 71% 129 22% 46 78% 161 23% 76 77% 257 32% 9 68% 19 34% 21 66% 41 18% 32 82% 143 Uganda 19% 50 81% 214 24% 12 76% 37 41% 18 59% 26 33% 38 67% 78 23% 9 77% 30 50% 1 50% 1 15% 4 85% 22 0% 0 0% 0 United Kingdom 19% 4 81% 17 21% 8 79% 31 41% 19 59% 27 41% 9 59% 13 18% 3 82% 14 100% 1 0% 0 27% 8 73% 22 40% 4 60% 6 United States of America 30% 102 70% 236 35% 36 65% 67 25% 22 75% 65 45% 85 55% 105 33% 15 67% 30 50% 1 50% 1 38% 27 62% 44 0% 0 0% 0 Uruguay 27% 138 73% 378 12% 6 88% 45 46% 19 54% 22 29% 12 71% 30 26% 48 74% 137 0% 0 0% 0 1% 1 99% 109 0% 0 0% 0 Venezuela 17% 33 83% 158 13% 4 87% 27 27% 12 73% 32 13% 5 88% 35 18% 3 82% 14 50% 2 50% 2 28% 5 72% 13 0% 0 100% 4 Vietnam 21% 5 79% 19 29% 9 71% 22 16% 3 84% 16 30% 15 70% 35 42% 5 58% 7 67% 2 33% 1 67% 4 33% 2 38% 6 63% 10 Zambia 100% 7 0% 0 0% 0 0% 0 100% 1 0% 0 67% 2 33% 1 100% 4 0% 0 100% 4 0% 0 67% 2 33% 1 33% 1 67% 2 Zimbabwe 19% 12 81% 52 9% 4 91% 43 38% 8 62% 13 26% 23 74% 67 25% 16 75% 48 100% 1 0% 0 48% 13 52% 14 0% 0 0% 0 GMMP2020 87 5. 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Function of subjects & sources in newspaper, television and radio news DO NOT KNOW SUBJECT SPOKESPERSON EXPERT OR COMMENTATOR PERSONAL EXPERIENCE EYEWITNESS POPULAR OPINION OTHER APPENDIX 5-6 Female % N Male % N Female % N Male % N Female % N Male % N Female % N Male % N Female % N Male % N Female % N Male % N Female % N Male % N Female % N Male % N Antigua and Barbuda 0% 0 0% 0 33% 2 67% 4 0% 0 100% 11 0% 0 100% 3 100% 1 0% 0 0% 0 0% 0 50% 1 50% 1 0% 0 0% 0 Argentina 14% 10 86% 61 21% 67 79% 257 21% 20 79% 76 21% 11 79% 41 33% 8 67% 16 36% 4 64% 7 33% 3 67% 6 10% 5 90% 44 Australia 0% 0 0% 0 28% 161 72% 405 26% 62 74% 179 34% 74 66% 145 50% 60 50% 59 57% 4 43% 3 35% 6 65% 11 57% 4 43% 3 Austria 0% 0 0% 0 27% 10 73% 27 28% 11 73% 29 6% 1 94% 16 14% 1 86% 6 0% 0 100% 1 100% 1 0% 0 100% 2 0% 0 Bangladesh 100% 1 0% 0 28% 57 72% 150 5% 10 95% 183 9% 12 91% 119 25% 14 75% 43 0% 0 100% 3 20% 3 80% 12 100% 3 0% 0 Belgium - French and Flemish 50% 1 50% 1 24% 57 76% 182 28% 23 72% 60 11% 5 89% 39 35% 12 65% 22 50% 7 50% 7 38% 5 62% 8 100% 1 0% 0 Benin 0% 0 100% 2 20% 3 80% 12 24% 9 76% 29 16% 5 84% 26 45% 18 55% 22 28% 5 72% 13 0% 0 0% 0 0% 0 0% 0 Bolivia 17% 5 83% 24 22% 44 78% 156 21% 65 79% 238 25% 19 75% 57 33% 13 67% 26 30% 9 70% 21 42% 23 58% 32 25% 1 75% 3 Bosnia and Herzegovina 0% 0 100% 1 11% 15 89% 117 23% 8 77% 27 21% 44 79% 168 42% 13 58% 18 33% 1 67% 2 0% 0 0% 0 0% 0 0% 0 Botswana 0% 0 0% 0 21% 3 79% 11 24% 7 76% 22 50% 4 50% 4 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 Brazil 43% 9 57% 12 24% 64 76% 206 16% 24 84% 127 25% 27 75% 81 59% 44 41% 31 43% 6 57% 8 29% 5 71% 12 27% 18 73% 48 Bulgaria 0% 0 100% 1 38% 18 63% 30 25% 3 75% 9 29% 5 71% 12 25% 1 75% 3 0% 0 100% 2 0% 0 0% 0 0% 0 0% 0 Burkina Faso 0% 0 100% 2 16% 28 84% 145 19% 16 81% 68 21% 6 79% 22 0% 0 100% 2 13% 6 88% 42 25% 1 75% 3 0% 0 0% 0 Cambodia 0% 0 0% 0 29% 5 71% 12 25% 4 75% 12 33% 1 67% 2 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 33% 1 67% 2 Cameroon 13% 6 88% 42 15% 13 85% 72 18% 14 82% 63 21% 5 79% 19 25% 1 75% 3 30% 3 70% 7 58% 7 42% 5 0% 0 0% 0 Canada 0% 0 0% 0 27% 104 73% 278 30% 37 70% 85 35% 48 65% 89 43% 20 57% 27 67% 2 33% 1 0% 0 100% 2 100% 2 0% 0 Cayman Islands 0% 0 0% 0 25% 4 75% 12 45% 19 55% 23 33% 1 67% 2 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 50% 1 50% 1 Central African Republic 0% 0 100% 5 13% 3 87% 20 100% 1 0% 0 0% 0 100% 1 0% 0 100% 1 0% 0 0% 0 0% 0 100% 2 0% 0 0% 0 Chad 0% 0 0% 0 0% 0 100% 2 29% 10 71% 24 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 Chile 22% 2 78% 7 26% 63 74% 180 22% 79 78% 277 29% 28 71% 69 32% 25 68% 53 0% 0 100% 7 45% 22 55% 27 11% 2 89% 17 People's Republic of China 100% 2 0% 0 32% 24 68% 51 20% 24 80% 94 27% 13 73% 35 39% 19 61% 30 22% 2 78% 7 0% 0 100% 2 14% 1 86% 6 Colombia 0% 0 100% 5 24% 29 76% 92 26% 14 74% 39 22% 11 78% 40 0% 0 100% 4 0% 0 100% 11 29% 2 71% 5 0% 0 0% 0 Congo 0% 0 0% 0 0% 0 0% 0 100% 6 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 Congo (Democratic Republic of the) 17% 1 83% 5 26% 9 74% 26 14% 8 86% 49 19% 3 81% 13 29% 5 71% 12 13% 1 88% 7 33% 6 67% 12 11% 2 89% 16 Costa Rica 44% 8 56% 10 29% 34 71% 85 27% 39 73% 106 31% 36 69% 81 61% 11 39% 7 42% 10 58% 14 9% 2 91% 21 0% 0 100% 1 Cuba 0% 0 100% 1 15% 16 85% 94 22% 16 78% 57 44% 8 56% 10 29% 2 71% 5 0% 0 0% 0 50% 2 50% 2 30% 6 70% 14 Cyprus 0% 0 0% 0 19% 66 81% 274 14% 5 86% 31 20% 8 80% 32 45% 9 55% 11 33% 3 67% 6 100% 1 0% 0 0% 0 0% 0 Denmark 0% 0 0% 0 29% 4 71% 10 36% 57 64% 103 25% 22 75% 67 58% 21 42% 15 0% 0 0% 0 40% 4 60% 6 0% 0 0% 0 Dominica 0% 0 0% 0 33% 2 67% 4 38% 3 63% 5 33% 1 67% 2 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 Dominican Republic 0% 0 100% 2 14% 8 86% 48 20% 10 80% 40 13% 3 88% 21 100% 2 0% 0 40% 6 60% 9 58% 7 42% 5 38% 8 62% 13 Ecuador 0% 0 0% 0 20% 71 80% 276 28% 21 72% 53 26% 14 74% 39 33% 6 67% 12 8% 1 92% 12 52% 12 48% 11 0% 0 0% 0 Egypt 0% 0 0% 0 12% 9 88% 66 11% 11 89% 89 11% 3 89% 25 40% 4 60% 6 0% 0 100% 4 0% 0 0% 0 0% 0 0% 0 El Salvador 15% 2 85% 11 17% 9 83% 43 30% 11 70% 26 20% 11 80% 45 17% 2 83% 10 67% 4 33% 2 60% 6 40% 4 27% 6 73% 16 Estonia 0% 0 0% 0 21% 5 79% 19 9% 2 91% 21 24% 13 76% 42 55% 11 45% 9 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Eswatini 0% 0 100% 2 24% 8 76% 26 29% 12 71% 29 30% 3 70% 7 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 Ethiopia 0% 0 100% 2 0% 0 100% 15 14% 8 86% 50 8% 3 92% 36 40% 2 60% 3 17% 1 83% 5 9% 1 91% 10 25% 1 75% 3 Fiji 100% 1 0% 0 25% 10 75% 30 22% 8 78% 29 31% 5 69% 11 67% 2 33% 1 100% 1 0% 0 0% 0 0% 0 50% 1 50% 1 Finland 50% 1 50% 1 25% 29 75% 89 34% 49 66% 96 26% 14 74% 39 56% 19 44% 15 50% 2 50% 2 64% 7 36% 4 100% 1 0% 0 91 DO NOT KNOW SPOKESPERSON EXPERT OR COMMENTATOR PERSONAL EXPERIENCE POPULAR OPINION APPENDIX 5-6 Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N France 25% 2 75% 6 27% 153 73% 411 25% 54 75% 163 24% 41 76% 127 46% 32 54% 37 39% 29 61% 45 15% 4 85% 23 5% 1 95% 20 Gabon 33% 1 67% 2 0% 0 0% 0 100% 1 0% 0 50% 2 50% 2 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 50% 1 50% 1 Gambia 0% 0 0% 0 25% 1 75% 3 12% 3 88% 23 18% 2 82% 9 0% 0 100% 1 0% 0 0% 0 0% 0 100% 2 0% 0 0% 0 Georgia 67% 2 33% 1 30% 65 70% 152 26% 66 74% 187 47% 20 53% 23 40% 10 60% 15 47% 9 53% 10 31% 4 69% 9 67% 2 33% 1 Ghana 0% 0 0% 0 18% 39 82% 180 13% 74 87% 494 14% 10 86% 64 46% 12 54% 14 0% 0 100% 19 24% 12 76% 38 0% 0 0% 0 Greenland 0% 0 100% 1 42% 5 58% 7 39% 23 61% 36 75% 3 25% 1 50% 5 50% 5 0% 0 100% 2 50% 3 50% 3 0% 0 0% 0 Grenada 0% 0 0% 0 34% 11 66% 21 36% 13 64% 23 0% 0 100% 2 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 67% 4 33% 2 Guatemala 50% 1 50% 1 23% 39 77% 128 14% 10 86% 63 34% 14 66% 27 20% 3 80% 12 50% 5 50% 5 50% 2 50% 2 40% 2 60% 3 Guinea 0% 0 0% 0 0% 0 0% 0 31% 4 69% 9 0% 0 100% 3 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 100% 6 Guyana 0% 0 0% 0 13% 3 87% 20 0% 0 100% 16 0% 0 100% 6 0% 0 0% 0 0% 0 0% 0 0% 0 100% 2 0% 0 0% 0 Haiti 8% 1 92% 11 25% 9 75% 27 19% 15 81% 64 13% 4 87% 26 100% 3 0% 0 70% 7 30% 3 50% 1 50% 1 7% 9 93% 121 Hong Kong SAR PRC 0% 0 0% 0 24% 40 76% 127 23% 27 77% 90 18% 15 82% 69 71% 10 29% 4 0% 0 100% 1 67% 6 33% 3 0% 0 0% 0 Iceland 0% 0 0% 0 18% 5 82% 23 42% 26 58% 36 18% 2 82% 9 50% 3 50% 3 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 India 19% 3 81% 13 18% 97 82% 446 8% 19 92% 220 8% 4 92% 45 27% 6 73% 16 0% 0 100% 19 8% 3 93% 37 20% 1 80% 4 Indonesia 0% 0 100% 1 10% 5 90% 43 11% 11 89% 92 24% 7 76% 22 50% 2 50% 2 30% 3 70% 7 33% 1 67% 2 100% 1 0% 0 Irag 0% 0 0% 0 43% 3 57% 4 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 Ireland 0% 0 0% 0 31% 34 69% 74 17% 14 83% 68 31% 15 69% 34 42% 10 58% 14 50% 2 50% 2 0% 0 100% 2 0% 0 100% 2 Israel 0% 0 0% 0 13% 27 87% 174 0% 0 100% 6 11% 7 89% 54 19% 3 81% 13 0% 0 100% 8 0% 0 100% 3 0% 0 0% 0 Italy 29% 2 71% 5 24% 73 76% 232 30% 34 70% 81 12% 9 88% 67 33% 3 67% 6 0% 0 100% 4 63% 5 38% 3 50% 1 50% 1 Jamaica 0% 0 0% 0 32% 25 68% 53 21% 16 79% 59 43% 13 57% 17 55% 17 45% 14 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 Japan 50% 3 50% 3 18% 19 82% 85 4% 3 96% 77 13% 1 88% 7 29% 16 71% 39 0% 0 0% 0 50% 10 50% 10 100% 3 0% 0 Jordan 0% 0 100% 5 20% 29 80% 113 14% 23 86% 141 20% 17 80% 69 13% 2 88% 14 3% 1 97% 32 0% 0 100% 7 100% 1 0% 0 Kenya 0% 0 0% 0 7% 5 93% 62 18% 13 82% 58 17% 7 83% 34 38% 12 63% 20 29% 7 71% 17 0% 0 0% 0 0% 0 0% 0 Kyrgyzstan 17% 1 83% 5 16% 11 84% 58 21% 11 79% 42 42% 5 58% 7 20% 7 80% 28 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 tebanon 0% 0 0% 0 14% 14 86% 84 13% 4 87% 26 24% 4 76% 13 25% 1 75% 3 0% 0 100% 5 40% 2 60% 3 0% 0 0% 0 tuxembourg 33% 1 67% 2 15% 13 85% 75 33% 15 67% 31 20% 3 80% 12 17% 1 83% 5 19% 6 81% 26 60% 3 40% 2 0% 0 100% 10 Macao 0% 0 0% 0 22% 16 78% 58 27% 23 73% 61 13% 2 88% 14 68% 13 32% 6 50% 2 50% 2 0% 0 0% 0 0% 0 100% 2 Malawi 0% 0 0% 0 25% 18 75% 54 19% 20 81% 88 33% 22 67% 44 36% 4 64% 7 40% 2 60% 3 33% 1 67% 2 83% 5 17% 1 Malaysia 0% 0 100% 1 15% 49 85% 278 13% 21 87% 140 10% 4 90% 37 43% 9 57% 12 33% 2 67% 4 29% 2 71% 5 0% 0 0% 0 Mali 9% 9 91% 91 10% 8 90% 74 12% 3 88% 23 20% 6 80% 24 17% 2 83% 10 8% 2 92% 23 8% 1 92% 12 18% 2 82% 9 Malta 60% 3 40% 2 24% 80 76% 260 25% 17 75% 50 36% 33 64% 59 59% 16 41% 11 42% 5 58% 7 50% 3 50% 3 17% 3 83% 15 Mexico 33% 3 67% 6 31% 209 69% 457 31% 56 69% 123 18% 5 82% 23 42% 5 58% 7 43% 3 57% 4 83% 5 17% 1 18% 8 82% 37 Moldova 63% 5 38% 3 27% 29 73% 80 31% 20 69% 44 40% 32 60% 48 42% 31 58% 42 37% 7 63% 12 40% 4 60% 6 40% 2 60% 3 Mongolia 7% 1 93% 13 19% 20 81% 84 7% 1 93% 14 31% 27 69% 60 45% 14 55% 17 33% 1 67% 2 20% 3 80% 12 0% 0 0% 0 Morocco 9% 4 91% 42 25% 27 75% 80 5% 4 95% 69 18% 3 82% 14 46% 6 54% 7 0% 0 100% 4 50% 1 50% 1 23% 3 77% 10 Myanmar 25% 1 75% 3 5% 2 95% 40 23% 13 77% 44 50% 1 50% 1 21% 3 79% 11 0% 0 0% 0 0% 0 100% 10 0% 0 100% 4 Namibia 75% 3 25% 1 29% 7 71% 17 37% 11 63% 19 23% 3 77% 10 50% 5 50% 5 50% 1 50% 1 0% 0 0% 0 0% 0 100% 1 Nepal 18% 11 82% 49 27% 95 73% 251 14% 21 86% 125 20% 18 80% 73 22% 20 78% 69 24% 4 76% 13 31% 15 69% 34 31% 9 69% 20 Netherlands 38% 3 63% 5 25% 49 75% 148 33% 18 67% 37 21% 14 79% 52 41% 11 59% 16 50% 1 50% 1 0% 0 100% 7 58% 14 42% 10 New Zealand 0% 0 0% 0 36% 25 64% 45 35% 27 65% 51 34% 11 66% 21 27% 9 73% 24 0% 0 100% 2 0% 0 0% 0 0% 0 0% 0 Nicaragua 0% 0 0% 0 17% 5 83% 24 32% 10 68% 21 67% 6 33% 3 50% 1 50% 1 42% 5 58% 7 25% 2 75% 6 0% 0 100% 2 GMMP2020 92 Who Makes the News? (p^ DO NOT KNOW SPOKESPERSON EXPERT OR COMMENTATOR PERSONAL EXPERIENCE POPULAR OPINION APPENDIX 5-6 Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N Niger 0% 0 0% 0 21% 3 79% 11 67% 6 33% 3 33% 1 67% 2 0% 0 0% 0 0% 0 100% 2 0% 0 0% 0 0% 0 0% 0 Nigeria 9% 2 91% 21 13% 21 87% 142 11% 13 89% 102 17% 25 83% 122 33% 1 67% 2 0% 0 100% 3 0% 0 100% 3 50% 1 50% 1 Norway 0% 0 0% 0 29% 30 71% 74 29% 63 71% 151 29% 18 71% 45 48% 16 52% 17 50% 1 50% 1 56% 22 44% 17 0% 0 100% 2 Pakistan 29% 2 71% 5 18% 90 82% 423 16% 19 84% 102 12% 3 88% 22 0% 0 100% 2 0% 0 0% 0 15% 5 85% 28 75% 3 25% 1 Palestine 0% 0 0% 0 22% 13 78% 46 12% 11 88% 84 13% 6 87% 39 27% 4 73% 11 0% 0 100% 3 0% 0 0% 0 0% 0 0% 0 Papua New Guinea 50% 1 50% 1 19% 4 81% 17 18% 3 82% 14 13% 1 88% 7 0% 0 100% 6 50% 1 50% 1 0% 0 100% 1 0% 0 100% 2 Paraguay 12% 4 88% 29 19% 20 81% 85 24% 16 76% 51 10% 5 90% 43 31% 5 69% 11 0% 0 100% 2 38% 3 63% 5 0% 0 100% 2 Peru 42% 10 58% 14 32% 104 68% 217 22% 25 78% 88 18% 16 82% 72 59% 13 41% 9 56% 23 44% 18 69% 9 31% 4 57% 4 43% 3 Poland 40% 2 60% 3 27% 43 73% 115 19% 43 81% 180 21% 68 79% 259 38% 23 62% 38 63% 30 38% 18 50% 25 50% 25 0% 0 100% 1 Portugal 57% 4 43% 3 27% 39 73% 105 31% 38 69% 86 24% 5 76% 16 55% 35 45% 29 67% 2 33% 1 50% 3 50% 3 0% 0 0% 0 Puerto Rico 50% 4 50% 4 32% 39 68% 84 33% 16 67% 32 42% 16 58% 22 25% 2 75% 6 0% 0 0% 0 0% 0 0% 0 41% 20 59% 29 Romania 100% 1 0% 0 30% 153 70% 350 29% 16 71% 40 38% 27 63% 45 52% 32 48% 30 47% 7 53% 8 53% 19 47% 17 100% 2 0% 0 Russian Federation 0% 0 100% 5 23% 17 77% 57 24% 11 76% 35 22% 11 78% 39 38% 11 62% 18 50% 7 50% 7 11% 1 89% 8 33% 2 67% 4 Senegal 33% 1 67% 2 18% 15 82% 70 13% 1 88% 7 13% 3 88% 21 50% 1 50% 1 0% 0 100% 1 0% 0 0% 0 0% 0 100% 1 Serbia 0% 0 100% 1 18% 58 82% 260 14% 3 86% 19 14% 8 86% 49 46% 6 54% 7 14% 1 86% 6 44% 4 56% 5 0% 0 0% 0 Seychelles 0% 0 100% 1 22% 4 78% 14 18% 2 82% 9 75% 3 25% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Sierra Leone 0% 0 0% 0 67% 4 33% 2 67% 2 33% 1 50% 1 50% 1 100% 3 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 South Africa 0% 0 100% 5 41% 22 59% 32 33% 36 67% 73 33% 14 67% 29 57% 16 43% 12 0% 0 0% 0 50% 1 50% 1 0% 0 0% 0 South Sudan 0% 0 0% 0 18% 3 82% 14 19% 8 81% 35 5% 1 95% 18 67% 2 33% 1 100% 1 0% 0 25% 1 75% 3 0% 0 100% 2 Spain 25% 7 75% 21 22% 112 78% 402 34% 76 66% 146 34% 30 66% 57 50% 63 50% 62 29% 12 71% 29 50% 44 50% 44 24% 9 76% 29 Suriname 0% 0 100% 1 22% 5 78% 18 31% 11 69% 25 50% 4 50% 4 67% 2 33% 1 0% 0 100% 1 88% 7 13% 1 0% 0 100% 2 Sweden 0% 0 100% 2 42% 66 58% 90 37% 103 63% 173 21% 15 79% 56 46% 37 54% 43 20% 1 80% 4 50% 3 50% 3 27% 7 73% 19 Switzerland 0% 0 0% 0 28% 143 72% 368 28% 47 72% 122 20% 35 80% 138 41% 42 59% 60 50% 4 50% 4 100% 1 0% 0 17% 1 83% 5 Taiwan Province of China 50% 1 50% 1 29% 81 71% 197 25% 45 75% 136 20% 31 80% 127 34% 11 66% 21 40% 4 60% 6 53% 28 47% 25 54% 15 46% 13 Tanzania 100% 1 0% 0 32% 34 68% 71 18% 17 82% 75 22% 15 78% 52 68% 15 32% 7 0% 0 100% 1 36% 18 64% 32 0% 0 0% 0 Togo 29% 4 71% 10 49% 19 51% 20 22% 10 78% 35 17% 4 83% 19 33% 1 67% 2 67% 2 33% 1 50% 12 50% 12 29% 6 71% 15 Trinidad and Tobago 0% 0 0% 0 38% 15 63% 25 24% 11 76% 35 20% 5 80% 20 50% 2 50% 2 50% 1 50% 1 0% 0 0% 0 0% 0 100% 2 Tunisia 33% 7 67% 14 21% 62 79% 235 13% 10 87% 66 26% 6 74% 17 32% 6 68% 13 25% 1 75% 3 0% 0 0% 0 25% 6 75% 18 Turkey 19% 4 81% 17 24% 140 76% 442 10% 21 90% 196 19% 24 81% 105 32% 47 68% 98 20% 20 80% 82 39% 7 61% 11 22% 7 78% 25 Uganda 50% 1 50% 1 24% 116 76% 359 8% 2 92% 24 47% 8 53% 9 33% 2 67% 4 33% 2 67% 4 14% 1 86% 6 0% 0 0% 0 United Kingdom 100% 1 0% 0 30% 84 70% 196 30% 79 70% 183 26% 60 74% 175 45% 56 55% 68 55% 6 45% 5 44% 22 56% 28 13% 1 88% 7 United States of America 50% 2 50% 2 31% 124 69% 274 26% 34 74% 95 41% 72 59% 105 48% 52 52% 56 10% 1 90% 9 30% 3 70% 7 0% 0 0% 0 Uruguay 38% 3 63% 5 23% 134 77% 439 24% 11 76% 35 28% 17 72% 44 62% 8 38% 5 38% 3 63% 5 0% 0 0% 0 20% 48 80% 188 Venezuela 38% 6 63% 10 17% 27 83% 130 19% 25 81% 105 11% 2 89% 16 0% 0 100% 5 0% 0 100% 10 27% 3 73% 8 50% 1 50% 1 Vietnam 100% 2 0% 0 36% 14 64% 25 16% 3 84% 16 27% 15 73% 41 29% 4 71% 10 42% 8 58% 11 18% 2 82% 9 100% 1 0% 0 Zambia 0% 0 100% 1 88% 15 12% 2 100% 1 0% 0 100% 3 0% 0 100% 1 0% 0 0% 0 100% 1 100% 1 0% 0 0% 0 0% 0 Zimbabwe 20% 1 80% 4 23% 41 77% 137 20% 21 80% 85 33% 1 67% 2 63% 12 37% 7 33% 1 67% 2 0% 0 0% 0 0% 0 0% 0 GMMP2020 93 Who Makes the News? (p^ 7. Subjects & sources in newspaper, television and radio news described as victims APPENDIX 5-7 VICTIM NOTAVICTIM Female % N Male % N Female % N Male % N Antigua and Barbuda 100% 1 0% 0 14% 3 86% 19 Argentina 25% 7 75% 21 20% 123 80% 487 Australia 50% 60 50% 60 31% 344 69% 765 Austria 100% 2 0% 0 23% 24 77% 79 Bangladesh 27% 11 73% 30 16% 93 84% 484 Belgium - French and Flemish 54% 14 46% 12 24% 99 76% 308 Benin 0% 0 0% 0 28% 40 72% 104 Bolivia 42% 11 58% 15 24% 170 76% 547 Bosnia and Herzegovina 0% 0 100% 8 20% 81 80% 324 Botswana 0% 0 0% 0 27% 14 73% 38 Brazil 63% 19 37% 11 26% 179 74% 517 Bulgaria 56% 5 44% 4 29% 22 71% 53 Burkina Faso 0% 0 0% 0 17% 57 83% 284 Cambodia 27% 4 73% 11 29% 9 71% 22 Cameroon 38% 3 63% 5 18% 47 82% 208 Canada 43% 3 57% 4 31% 213 69% 480 Cayman Islands 0% 0 0% 0 40% 26 60% 39 Central African Republic 0% 0 100% 2 12% 4 88% 29 Chad 50% 1 50% 1 30% 11 70% 26 Chile 15% 5 85% 28 26% 215 74% 619 People's Republic of China 44% 4 56% 5 27% 83 73% 220 Colombia 69% 9 31% 4 22% 54 78% 189 Congo 100% 2 0% 0 100% 6 0% 0 Congo (Democratic Republic of the) 42% 5 58% 18% 29 82% 128 Costa Rica 72% 21 28% 8 28% 122 72% 316 Cuba 0% 0 0% 0 21% 50 79% 183 Cyprus 56% 5 44% 4 20% 91 80% 353 Denmark 67% 6 33% 3 34% 101 66% 198 Dominica 0% 0 0% 0 33% 6 67% 12 Dominican Republic 88% 7 13% 1 23% 41 77% 137 Ecuador 39% 11 61% ~ir 23% 116 77% 389 Egypt 14% 1 86% 6 12% 26 88% 184 El Salvador 45% 10 55% 12 23% 44 77% 147 Estonia 0% 0 0% 0 0% 0 0% 0 Eswatini 0% 0 100% 2 27% 22 73% 60 Ethiopia 0% 0 100% 11% 15 89% 123 Fiji 0% 0 0% _____ 0 28% 29 72% 73 Finland 33% 4 67% 8 33% 118 67% 238 France 63% 35 38% 21 26% 293 74% 816 Gabon 0% 0 100% 2 60% 6 40% 4 Gambia 0% 0 100% 3 15% 6 85% 35 Georgia 33% 3 67% 6 30% 175 70% 403 Ghana 5% 3 95% 60 16% 147 84% 771 94 APPENDIX 5-7 VICTIM NOTAVICTIM Female % N Male % N Female % N Male % N Greenland 0% 0 0% 0 41% 39 59% 55 Grenada 0% 0 0% 38% 28 62% 46 Guatemala 46% 18 54% 21 22% 63 78% 220 Guinea 0% 0 0% 0 22% 5 78% 18 Guyana 0% 0 100% 5 10% 3 90% 28 Haiti 33% 6 67% ~ 16% 45 84% 240 Hong Kong SAR PRC 0% 0 0% 0 0% 0 0% 0 Iceland 0% 0 100% l 34% 36 66% 70 India 48% 27 52% 29 13% 116 87% 779 Indonesia 25% 2 75% 6 15% 29 85% 164 Irag 0% 0 0% 0 0% 0 0% 0 Ireland 67% 6 33% 3 27% 73 73% 196 Israel 60% 6 40% 4 12% 35 88% 258 Italy 67% 28 33% 14 22% 107 78% 388 Jamaica 32% 8 68% 17 33% 62 67% 124 Japan 69% 11 31% 5 18% 47 82% 217 Jordan 8% 2 92% 22 16% 72 84% 366 Kenya 29% 5 71% 12 19% 43 81% 182 Kyrgyzstan 60% 3 40% 2 19% 33 81% 142 Lebanon 100% 2 0% 0 15% 23 85% 133 Luxembourg 0% 0 100% 3 21% 42 79% 159 Macao 0% 0 0% 0 0% 0 0% 0 Malawi 0% 0 0% 27% 72 73% 199 Malaysia 50% 8 50% 8 15% 80 85% 471 Mali 50% 10 50% 10 9% 25 91% 247 Malta 53% 29 47% 26 26% 137 74% 389 Mexico 63% 46 37% 27 29% 254 71% 636 Moldova 56% 14 44% 11 34% 122 66% 232 Mongolia 67% 4 33% 2 25% 67 75% 200 Morocco 44% 7 56% 9 16% 43 84% 222 Myanmar 0% 0 0% 0 0% 0 0% 0 Namibia 0% 0 0% 0 0% 0 0% 0 Nepal 57% 46 43% 35 22% 167 78% 606 Netherlands 0% 0 0% 0 32% 105 68% 225 New Zealand 43% 9 57% 12 34% 67 67% 133 Nicaragua 100% 2 0% 0 30% 28 70% 64 Niger 100% 1 0% 0 17% 1 83% 5 Nigeria 33% 5 67% 10 13% 61 87% 393 Norway 45% 9 55% 11 32% 146 68% 305 Pakistan 43% 15 57% 20 16% 108 84% 566 Palestine 10% 2 90% 19 16% 32 84% 163 Papua New Guinea 33% 1 67% 2 20% 2 80% 8 Paraguay 0% 0 0% 0 0% 0 0% 0 Peru 0% 0 0% _2_ 0% 0 100% 1 Poland 46% 17 54% 20 27% 226 73% 616 Portugal 50% 6 50% 6 34% 121 66% 239 GMMP2020 95 Who Makes the News? (\\\ APPENDIX 5-7 VICTIM NOTAVICTIM Female % N Male % N Female % N Male % N Puerto Rico 79% 15 21% 4 33% 84 67% 173 Romania 53% 21 48% 19 33% 238 67% 477 Russian Federation 0% 0 0% 0 0% 0 0% 0 Senegal 33% 1 67% 2 17% 21 83% 101 Serbia 14% 3 86% 19 19% 79 81% 338 Seychelles 0% 0 0% 0 26% 9 74% 25 Sierra Leone 80% 4 20% 1 83% 5 17% 1 South Africa 80% 4 20% 1 37% 89 63% 152 South Sudan 100% 3 0% 0 17% 15 83% 75 Spain 61% 40 39% 26 30% 336 70% 766 Suriname 0% 0 100% 2 35% 28 65% 52 Sweden 42% 19 58% 26 37% 224 63% 381 Switzerland 52% 27 48% 25 28% 262 72% 690 Taiwan Province of China 26% 12 74% 35 29% 204 71% 495 Tanzania 36% 9 64% 16 29% 95 71% 231 Togo 67% 4 33% 2 33% 53 67% 107 Trinidad and Tobago 50% 7 50% 7 25% 26 75% 78 Tunisia 55% 43 45% 35 14% 53 86% 329 Turkey 31% 94 69% 209 19% 209 81% 874 Uganda 43% 17 58% 23 23% 116 77% 384 United Kingdom 48% 29 52% 31 31% 284 69% 640 United States of America 46% 46 54% 54 34% 275 66% 537 Uruguay 38% 16 62% 26 23% 212 77% 704 Venezuela 24% 4 76% 13 17% 49 83% 245 Vietnam 40% 4 60% 6 30% 49 70% 112 Zambia 0% 0 0% 0 0% 0 0% 0 Zimbabwe 0% 0 0% 0 0% 0 0% 0 8. Subjects & sources in newspaper, television and radio news, mentioned by family status APPENDIX 5-8 YES Female Male Female Male % N % N V /o N % N Antigua and Barbuda 100% 1 0% 0 14% 3 86% 19 Argentina 57% 28 43% 21 17% 99 83% 488 Australia 56% 101 44% 80 27% 270 73% 725 Austria 60% 3 40% 2 23% 23 77% 77 Bangladesh 64% 32 36% 18 12% 68 88% 492 Belgium 50% 8 50% 8 25% 104 75% 312 Benin 100% 2 0% 0 27% 38 73% 104 Bolivia 56% 25 44% 20 22% 154 78% 538 GMMP2020 96 Who Makes the News? (rh APPENDIX 5-8 YES NO Female Male Female Male % N % N % N % N Bosnia and Herzegovina 42% 10 58% 14 18% 71 82% 320 Botswana 45% 5 55% 6 22% 9 78% 32 Brazil 52% 44 48% 40 24% 153 76% 486 Bulgaria 83% 5 17% 1 28% 22 72% 56 Burkina Faso 40% 2 60% 3 16% 55 84% 281 Cambodia 50% 3 50% 3 23% 6 77% 20 Cameroon 25% 3 75% 9 19% 46 81% 202 Canada 56% 33 44% 26 28% 180 72% 456 Cayman Islands 0% 0 100% 1 41% 26 59% 38 Central African Republic 100% 1 0% 0 9% 3 91% 29 Chad 50% 1 50% 1 29% 10 71% 25 Chile 50% 15 50% 15 25% 205 75% 619 People's Republic of China 39% 9 61% 14 26% 76 74% 211 Colombia 75% 6 25% 2 21% 51 79% 194 Congo 0% 0 0% 0 100% 7 0% 0 Congo (Democratic Republic of the) 55% 7 65% 13 17% 26 83% 126 Costa Rica 57% 17 43% 13 28% 123 72% 312 Cuba 39% 13 61% 20 19% 37 82% 163 Cyprus 35% 8 65% 15 20% 84 80% 339 Denmark 57% 4 43% 3 34% 103 66% 198 Dominica 0% 0 0% 0 33% 6 67% 12 Dominican Republic 100% 4 0% 0 22% 39 78% 138 Ecuador 65% 11 35% 6 22% 114 78% 397 Egypt 0% 0 100% 1 13% 27 87% 188 El Salvador 57% 4 43% 3 24% 47 77% 153 Estonia 67% 2 33% 1 22% 26 78% 90 Eswatini 75% 3 25% 1 26% 21 74% 60 Ethiopia 0% 0 100% 3 12% 16 88% 121 Fiji 67% 8 33% 4 23% 21 77% 69 Finland 62% 13 38% 8 31% 109 69% 238 France 60% 40 40% 27 26% 276 74% 805 Gabon 100% 1 0% 0 50% 5 50% 5 Gambia 0% 0 0% 0 14% 6 86% 38 Georgia 100% 1 0% 0 30% 176 70% 403 Ghana 78% 7 22% 2 15% 140 85% 807 Greenland 100% 1 0% 0 41% 38 59% 54 Grenada 0% 0 100% 2 38% 28 62% 46 Guatemala 52% 17 48% 16 21% 59 79% 222 Guinea 0% 0 0% 0 22% 5 78% 18 Guyana 0% 0 0% 0 8% 3 92% 35 Haiti 57% 4 43% 3 15% 45 85% 248 Hong Kong SAR PRC 100% 7 0% 0 23% 90 77% 295 Iceland 100% 2 0% 0 32% 33 68% 71 India 36% 31 64% 54 12% 102 88% 746 Indonesia 56% 5 44% 4 13% 25 87% 165 Iraq 0% 0 0% 0 50% 4 50% 4 Ireland 52% 11 48% 10 26% 64 74% 186 Israel 44% 12 56% 15 9% 25 91% 244 Italy 66% 27 34% 14 21% 100 79% 385 Jamaica 55% 16 45% 13 29% 55 71% 132 Japan 64% 16 36% 9 16% 39 84% 212 Jordan 45% 10 55% 12 15% 64 85% 368 97 Female Male Female Male % N % N % N % N Kenya 40% 4 60% 6 18% 40 82% 186 Kyrgyzstan 0% 0 100% 1 20% 35 80% 142 Lebanon 0% 0 0% 0 16% 25 84% 132 Luxembourg 33% 5 67% 10 19% 37 81% 153 Macao 71% 5 29% 2 27% 53 73% 141 Malawi 43% 3 57% 4 26% 69 74% 195 Malaysia 47% 24 53% 27 12% 63 88% 450 Mali 63% 5 38% 3 10% 28 90% 253 Malta 51% 23 49% 22 26% 137 74% 386 Mexico 43% 18 57% 24 30% 275 70% 631 Moldova 59% 16 41% 11 33% 112 67% 227 Mongolia 64% 9 36% 5 23% 58 77% 197 Morocco 50% 4 50% 4 16% 44 84% 223 Myanmar 33% 1 67% 2 15% 19 85% 111 Namibia 38% 3 63% 5 36% 27 64% 49 Nepal 52% 12 48% 11 23% 180 78% 620 Netherlands 65% 15 35% 8 27% 98 73% 271 New Zealand 50% 5 50% 5 33% 67 67% 139 Nicaragua 50% 2 50% 2 30% 27 70% 62 Niger 0% 0 100% 1 38% 9 63% 15 Nigeria 33% 3 67% 6 14% 61 86% 389 Norway 58% 15 42% 11 31% 135 69% 296 Pakistan 53% 49 47% 44 12% 73 88% 538 Palestine 25% 1 75% 3 15% 33 85% 182 Papua New Guinea 0% 0 100% 5 18% 10 82% 47 Paraguay 50% 5 50% 5 18% 48 82% 225 Peru 58% 32 42% 23 30% 172 70% 404 Poland 48% 26 52% 28 25% 208 75% 610 Portugal 71% 24 29% 10 31% 102 69% 232 Puerto Rico 60% 9 40% 6 34% 88 66% 171 Romania 49% 58 51% 60 31% 198 69% 431 Russian Federation 46% 12 54% 14 23% 48 77% 159 Senegal 25% 1 75% 3 17% 20 83% 101 Serbia 29% 7 71% 17 18% 73 82% 331 Seychelles 0% 0 0% 0 26% 9 74% 25 Sierra Leone 100% 1 0% 0 71% 10 29% 4 South Africa 62% 18 38% 11 33% 70 67% 143 South Sudan 100% 2 0% 0 15% 13 85% 75 Spain 59% 23 41% 16 30% 329 70% 770 Suriname 33% 2 67% 4 36% 27 64% 49 Sweden 51% 24 49% 23 36% 210 64% 367 Switzerland 49% 39 51% 41 26% 235 74% 660 Taiwan Province of China 39% 16 61% 25 29% 200 71% 501 Tanzania 72% 13 28% 5 27% 87 73% 233 Togo 33% 1 67% 2 34% 55 66% 106 Trinidad and Tobago 50% 9 50% 9 25% 28 75% 82 Tunisia 33% 3 67% 6 21% 92 79% 354 Turkey 43% 87 57% 117 17% 183 83% 863 Uganda 50% 13 50% 13 23% 117 77% 390 United Kingdom 50% 70 50% 70 29% 240 71% 597 United States of America 32% 35 68% 75 35% 253 65% 473 Uruguay 34% 30 66% 57 23% 194 77% 664 98 APPENDIX 5-8 YES NO Female Male Female Male % N % N % N % N Venezuela 73% 8 27% 3 17% 56 83% 282 Vietnam 50% 13 50%_ 13 27% 36 73% 98 Zambia 86% 6 14% 1 83% 15 17% 3 Zimbabwe 50% 3 50% 3 24% 74 76% 234 9. Subjects & sources quoted directly in newspapers YES NO APPENDIX 5-9 Female Male Female Male % N % N % N % N Antigua and Barbuda 0% 0 100% 6 0% 0 0% 0 Argentina 20% 24 80% 99 16% 37 84% 196 Australia 35% 120 65% 226 58% 119 62% 193 Austria 31% 11 69% 25 24% 7 76% 22 Bangladesh 11% 32 89% 259 23% 36 78% 124 Belgium 27% 26 73% 70 28% 33 72% 83 Benin 30% 14 70% 32 17% 5 83% 24 Bolivia 25% 25 75% 77 8% 5 92% 54 Bosnia and Herzegovina 31% 23 69% 52 25% 9 75% 27 Botswana 33% 6 67% 12 25% 2 75% 6 Brazil 28% 44 73% 116 27% 37 73% 100 Bulgaria 10% 1 90% 9 47% 8 53% 9 Burkina Faso 22% 34 78% 122 0% 0 100% 1 Cambodia 11% 1 89% 8 22% 2 78% 7 Cameroon 17% 15 83% 72 42% 5 58% 7 Canada 27% 59 73% 160 55% 50 65% 91 Cayman Islands 48% 16 52% 17 27% 4 73% 11 Central African Republic 0% 0 100% 12 0% 0 100% 4 Chad 11% 1 89% 8 0% 0 0% 0 Chile 38% 74 62% 123 10% 3 90% 26 People's Republic of China 29% 15 71% 37 11% 1 89% 8 Colombia 20% 10 80% 40 29% 19 71% 47 Congo 100% 3 0% 0 100% 1 0% 0 Congo (Democratic Republic of the) 18% 3 82% 14 0% 0 100% 1 Costa Rica 25% 34 75% 101 32% 23 68% 50 Cuba 18% 7 82% 32 22% 5 78% 18 Cyprus 20% 11 80% 44 25% 25 75% 76 Denmark 33% 54 67% 111 50% 3 50% 3 Dominica 67% 2 33% 1 50% 2 50% 2 Dominican Republic 20% 11 80% 43 22% 17 78% 61 Ecuador 26% 34 74% 97 25% 9 75% 27 Egypt 18% 7 82% 31 15% 11 87% 74 El Salvador 20% 10 80% 39 21% 3 79% 11 Estonia 17% 6 83% 29 10% 2 90% 19 Eswatini 22% 12 78% 42 36% 9 64% 16 Ethiopia 19% 6 81% 25 11% 1 89% 8 99 YES NO APPENDIX 5-9 Female % N Male % N Female % N Male % N Fiji 52% 15 48% 14 23% 5 77% 17 Finland 31% 45 69% 98 30% 41 70% 97 France 29% 66 71% 158 28% 47 72% 119 Gabon 50% 5 50% 5 100% 1 0% 0 Gambia 18% 2 82% 9 7% 1 93% 14 Georgia 13% 5 87% 34 24% 4 76% 13 Ghana 12% 19 88% 136 18% 26 82% 120 Greenland 47% 26 53% 29 0% 0 0% 0 Grenada 100% 2 0% 0 27% 6 73% 16 Guatemala 26% 19 74% 54 20% 9 80% 37 Guinea 25% 2 75% 6 0% 0 100% 1 Guyana 0% 0 100% 8 20% 2 80% 8 Haiti 0% 0 0% 0 0% 0 100% 2 Hong Kong SAR PRC 12% 8 88% 60 18% 39 82% 175 Iceland 44% 12 56% 15 0% 0 100% 3 India 11% 29 89% 242 18% 59 82% 263 Indonesia 16% 19 84% 97 14% 6 86% 38 Irag 0% 0 0% 0 0% 0 0% 0 Ireland 26% 30 74% 86 36% 16 64% 29 Israel 12% 8 88% 57 20% 17 80% 70 Italy 26% 42 74% 122 22% 39 78% 139 Jamaica 49% 30 51% 31 29% 7 71% 17 Japan 16% 12 84% 62 22% 8 78% 28 Jordan 40% 26 60% 39 28% 35 72% 91 Kenya 19% 21 81% 87 19% 16 81% 67 Kyrgyzstan 4% 1 96% 25 15% 7 85% 40 Lebanon 6% 2 94% 31 8% 1 92% 11 Luxembourg 20% 17 80% 70 20% 7 80% 28 Macao 20% 16 80% 64 30% 14 70% 32 Malawi 32% 29 68% 61 32% 12 68% 26 Malaysia 19% 21 81% 89 19% 30 81% 125 Mali 13% 19 87% 122 2% 1 98% 48 Malta 32% 34 68% 72 28% 86 72% 225 Mexico 31% 40 69% 91 27% 41 73% 113 Moldova 30% 7 70% 16 64% 7 36% 4 Mongolia 24% 17 76% 54 18% 7 82% 31 Morocco 16% 20 84% 103 0% 0 100% 1 Myanmar 23% 7 77% 23 0% 0 100% 10 Namibia 35% 22 65% 41 44% 8 56% 10 Nepal 18% 27 82% 126 30% 127 70% 292 Netherlands 22% 34 78% 119 37% 65 63% 111 New Zealand 41% 29 59% 42 43% 18 57% 24 Nicaragua 0% 0 0% 0 9% 1 91% 10 Niger 33% 1 67% 2 0% 0 0% 0 Nigeria 9% 8 91% 78 13% 17 87% 115 Norway 30% 63 70% 148 17% 6 83% 29 Pakistan 17% 29 83% 141 18% 37 82% 172 Palestine 6% 3 94% 50 17% 8 83% 40 Papua New Guinea 13% 2 87% 13 0% 0 0% 0 Paraguay 15% 14 85% 80 11% 5 89% 40 Peru 24% 18 76% 57 23% 18 77% 61 Poland 21% 17 79% 63 24% 12 76% 37 100 YES NO APPENDIX 5-9 Female % N Male % N Female % N Male % N Portugal 42% 36 58% 50 18% 6 82% 27 Puerto Rico 38% 20 62% 32 33% 44 67% 89 Romania 35% 27 65% 51 22% 47 78% 165 Russian Federation 33% 45 67% 93 16% 15 84% 80 Senegal 16% 7 84% 38 0% 0 0% 0 Serbia 18% 16 82% 71 15% 14 85% 78 Seychelles 31% 4 69% 9 0% 0 100% 6 Sierra Leone 100% 2 0% 0 0% 0 100% 1 South Africa 37% 47 63% 80 17% 3 83% 15 South Sudan 25% 10 75% 30 0% 0 100% 3 Spain 22% 30 78% 104 20% 40 80% 161 Suriname 0% 0 0% 0 0% 0 100% 3 Sweden 40% 135 60% 204 32% 52 68% 111 Switzerland 26% 114 74% 325 31% 99 69% 218 Taiwan Province of China 16% 10 84% 53 16% 18 84% 92 Tanzania 28% 35 72% 92 10% 1 90% 9 Togo 33% 15 67% 30 0% 0 100% 2 Trinidad and Tobago 22% 15 78% 52 35% 12 65% 22 Tunisia 26% 7 74% 20 20% 26 80% 102 Turkey 24% 33 76% 104 28% 35 72% 92 Uganda 29% 21 71% 51 23% 48 77% 160 United Kingdom 35% 172 65% 321 21% 25 79% 92 United States of America 37% 168 63% 285 31% 92 69% 202 Uruguay 36% 73 64% 131 23% 28 77% 95 Venezuela 25% 6 75% 18 20% 7 80% 28 Vietnam 42% 32 58% 45 25% 4 75% 12 Zambia 83% 5 17% 1 100% 6 0% 0 Zimbabwe 21% 22 79% 85 23% 38 77% 124 10. Subjects & sources appearing in newspaper photographs YES NO DO NOT KNOW APPENDIX 5-10 Female Male Female Male Female Male % N % N % N % N % N % N Antigua and Barbuda 0% 0 0% 0 0% 0 100% 6 0% 0 0% 0 Argentina 15% 6 85% 33 19% 62 81% 268 20% 1 80% 4 Australia 40% 71 60% 107 35% 166 65% 310 50% 2 50% 2 Austria 33% 4 67% 8 26% 14 74% 39 0% 0 0% 0 Bangladesh 17% 10 83% 50 15% 57 85% 333 0% 0 0% 0 Belgium 19% 9 81% 39 30% 50 70% 114 0% 0 0% 0 Benin 14% 3 86% 19 27% 8 73% 22 35% 8 65% 15 Bolivia 7% 3 93% 38 21% 24 79% 88 50% 1 50% 1 Bosnia and Herzegovina 29% 12 71% 29 22% 13 78% 45 54% 7 46% 6 Botswana 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Brazil 16% 8 84% 43 30% 70 70% 164 23% 3 77% 10 Bulgaria 37% 7 63% 12 33% 3 67% 6 0% 0 0% 0 GMMP2020 101 Who Makes the News? YES NO DO NOT KNOW APPENDIX 5-10 Female Male Female Male Female Male % N % N % N % N % N % N Burkina Faso 20% 20 80% 80 32% 13 68% 28 6% 1 94% 17 Cambodia 16% 3 84% 16 0% 0 100% 2 0% 0 0% 0 Cameroon 30% 9 70% 21 14% 9 86% 57 67% 2 33% 1 Canada 25% 2 75% 6 33% 15 67% 30 0% 0 0% 0 Cayman Islands 55% 11 45% 9 36% 9 64% 16 0% 0 100% 3 Central African Republic 0% 0 0% 0 0% 0 100% 16 0% 0 0% 0 Chad 0% 0 100% 1 14% 1 86% 6 0% 0 0% 0 Chile 58% 42 42% 30 22% 34 78% 119 100% 1 0% 0 People's Republic of China 31% 8 69% 18 22% 7 78% 25 33% 1 67% 2 Colombia 18% 3 82% 14 27% 26 73% 72 0% 0 100% 1 Congo 100% 4 0% 0 0% 0 0% 0 0% 0 0% 0 Congo (Democratic Republic of the) 18% 2 82% _9_ 14% 1 86% 6 0% 0 0% 0 Costa Rica 25% 17 75% 51 28% 39 72% 99 50% 1 50% 1 Cuba 25% 4 75% 12 17% 8 83% 38 0% 0 0% 0 Cyprus 0% 0 100% 25 27% 36 73% 95 0% 0 0% 0 Denmark 34% 10 66% 19 33% 47 67% 95 0% 0 0% 0 Dominica 80% 4 20% 1 0% 0 100% 2 0% 0 0% 0 Dominican Republic 18% 8 82% 36 25% 20 75% 61 0% 0 100% 7 Ecuador 23% 7 77% 23 26% 34 74% 98 33% 2 67% 4 Egypt 4% 1 96% 25 18% 17 82% 80 0% 0 0% 0 El Salvador 27% 3 73% _8_ 18% 9 82% 42 50% 1 50% 1 Estonia 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Eswatini 34% 11 66% 21 23% 11 77% 36 0% 0 0% 0 Ethiopia 25% 1 75% 3 17% 6 83% 30 0% 0 0% 0 Fiji 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Finland 31% 14 69% 31 31% 72 69% 164 0% 0 0% 0 France 39% 31 61% 49 26% 79 74% 227 100% 3 0% 0 Gabon 63% 5 38% 3 33% 1 67% 2 0% 0 0% 0 Gambia 6% 1 94% 17 33% 2 67% 4 0% 0 100% 2 Georgia 18% 6 82% 27 12% 3 88% 22 0% 0 0% 0 Ghana 17% 25 83% 119 13% 20 87% 139 0% 0 0% 0 Greenland 48% 13 52% 14 46% 13 54% 15 0% 0 0% 0 Grenada 29% 2 71% _5_ 20% 2 80% _8_ 100% 2 0% 0 Guatemala 17% 10 83% 49 29% 17 71% 42 100% 1 0% 0 Guinea 50% 2 50% 2 0% 0 100% 2 0% 0 100% 3 Guyana 8% 1 92% 12 0% 0 100% 1 0% 0 0% 0 Haiti 0% 0 0% 0 13% 1 88% 7 0% 0 0% 0 Hong Kong SAR PRC 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Iceland 40% 4 60% _6_ 42% 8 58% 11 0% 0 100% 1 India 17% 20 83% 100 15% 67 85% 377 3% 1 97% 28 Indonesia 29% 2 71% 5 12% 9 88% 65 0% 0 0% 0 Iraq 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Ireland 28% 17 72% 43 29% 29 71% 71 0% 0 0% 0 Israel 33% 7 67% 14 13% 17 87% 111 50% 1 50% 1 Italy 24% 16 76% 51 24% 65 76% 205 0% 0 100% 6 Jamaica 41% 12 59% 17 44% 24 56% 31 0% 0 0% 0 Japan 33% 10 67% 20 13% 10 87% 68 0% 0 0% 0 Jordan 41% 13 59% 19 30% 48 70% 111 0% 0 0% 0 Kenya 19% 7 81% 30 20% 30 80% 122 0% 0 100% 1 Kyrgyzstan 11% 2 89% 16 11% 6 89% 49 0% 0 0% 0 Lebanon 0% 0 100% 9 8% 3 92% 33 0% 0 0% 0 Luxembourg 11% 2 89% 16 22% 22 78% 79 0% 0 100% 3 Macao 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 GMMP2020 102 Who Makes the News? YES NO DO NOT KNOW APPENDIX 5-10 Female Male Female Male Female Male % N % N % N % N % N % N Malawi 42% 24 58% 33 23% 16 77% 54 100% 1 0% 0 Malaysia 21% 22 79% 82 18% 29 82% 129 0% 0 0% 0 Mali 5% 3 95% 56 12% 16 88% 121 33% 1 67% 2 Malta 30% 19 70% 44 29% 96 71% 234 21% 5 79% 19 Mexico 19% 17 81% 73 32% 60 68% 130 67% 4 33% 2 Moldova 38% 3 63% 5 48% 12 52% 13 0% 0 100% 2 Mongolia 0% 0 100% 25 28% 24 72% 61 0% 0 0% 0 Morocco 24% 4 76% 13 16% 15 84% 81 9% 1 91% 10 Myanmar 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Namibia 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Nepal 47% 21 53% 24 24% 108 76% 340 34% 16 66% 31 Netherlands 83% 5 17% 1 43% 29 57% 39 0% 0 0% 0 New Zealand 50% 10 50% 10 40% 36 60% 53 33% 1 67% 2 Nicaragua 0% 0 0% 0 9% 1 91% 10 0% 0 0% 0 Niger 33% 1 67% 2 0% 0 0% 0 0% 0 0% 0 Nigeria 15% 2 85% 11 12% 23 89% 177 0% 0 100% 11 Norway 37% 37 63% 64 22% 32 78% 112 0% 0 0% 0 Pakistan 18% 11 82% 49 17% 56 83% 268 0% 0 0% 0 Palestine 36% 4 64% 7 10% 6 90% 55 0% 0 0% 0 Papua New Guinea 0% 0 100% 8 29% 2 71% 5 0% 0 0% 0 Paraguay 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Peru 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Poland 38% 13 62% 21 16% 15 84% 78 0% 0 0% 0 Portugal 35% 14 65% 26 36% 27 64% 49 60% 3 40% 2 Puerto Rico 37% 15 63% 26 34% 49 66% 95 0% 0 0% 0 Romania 30% 33 70% 76 23% 43 77% 141 0% 0 0% 0 Russian Federation 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Senegal 0% 0 100% 16 23% 6 77% 20 0% 0 0% 0 Serbia 15% 12 85% 67 18% 18 82% 83 0% 0 0% 0 Seychelles 27% 4 73% 11 11% 1 89% 8 0% 0 0% 0 Sierra Leone 100% 2 0% 0 75% 3 25% 0% 0 0% 0 South Africa 38% 11 62% 18 34% 38 66% 75 0% 0 0% 0 South Sudan 20% 4 80% 16 26% 6 74% 17 0% 0 0% 0 Spain 14% 8 86% 49 22% 58 78% 208 0% 0 100% 1 Suriname 0% 0 0% 0 0% 0 100% 3 0% 0 0% 0 Sweden 47% 85 53% 96 32% 102 68% 218 0% 0 0% 0 Switzerland 33% 59 67% 118 27% 152 73% 421 40% 2 60% 3 Taiwan Province of China 18% 5 82% 23 16% 23 84% 122 0% 0 0% 0 Tanzania 13% 4 87% 26 28% 28 72% 72 0% 0 0% 0 Togo 44% 8 56% 10 21% 5 79% 19 67% 2 33% 1 Trinidad and Tobago 38% 10 62% 16 25% 17 75% 50 0% 0 0% 0 Tunisia 4% 2 96% 51 30% 33 70% 77 0% 0 0% 0 Turkey 31% 51 69% 113 17% 14 83% 67 27% 3 73% 8 Uganda 19% 7 81% 30 25% 62 75% 186 0% 0 0% 0 United Kingdom 40% 73 60% 111 29% 124 71% 300 0% 0 0% 0 United States of America 36% 41 64% 72 34% 216 66% 411 0% 0 0% 0 Uruguay 49% 18 51% 19 29% 82 71% 204 50% 1 50% 1 Venezuela 33% 2 67% 4 21% 11 79% 42 0% 0 0% 0 Vietnam 35% 7 65% 13 40% 29 60% 44 0% 0 0% 0 Zambia 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Zimbabwe 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 GMMP2020 103 Who Makes the News? 11. Presenters and reporters in newspaper, teLevision and radio news APPENDIX 5-11 PRINT RADIO TELEVISION Reporter Presenter Reporter Presenter Reporter Female Male Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N % N % N Antigua and Barbuda 100% 3 0% 0 54% 7 46% 6 100% 2 0% 0 0% 0 100% 3 0% 0 0% 0 Argentina 43% 23 57% 30 30% 30 70% 71 54% 14 46% 12 51% 26 49% 25 50% 13 50% 13 Australia 44% 66 56% 83 42% 31 58% 42 100% 3 0% 0 77% 134 23% 40 43% 38 57% 51 Austria 33% 6 67% 12 76% 13 24% 4 13% 2 87% 13 71% 15 29% 6 68% 13 32% 6 Bangladesh 5% 3 95% 53 65% 15 35% 8 0% 0 100% 2 77% 49 23% 15 21% 7 79% 27 Belgium 37% 15 63% 26 65% 31 35% 17 27% 3 73% 8 34% 32 66% 62 38% 24 63% 40 Benin 24% 8 76% 25 27% 8 73% 22 20% 1 80% 4 100% 9 0% 0 0% 0 100% 7 Bolivia 30% 10 70% 23 26% 16 74% 46 48% 20 52% 22 54% 124 46% 104 43% 77 57% 101 Bosnia and Herzegovina 30% 7 70% 16 100% 47 0% 0 39% 13 61% 20 97% 65 3% 2 67% 22 33% 11 Botswana 43% 12 57% 16 50% 14 50% 14 25% 1 75% 3 0% 0 100% 1 0% 0 0% 0 Brazil 49% 42 51% 44 40% 25 60% 38 49% 22 51% 23 55% 85 45% 70 42% 32 58% 44 Bulgaria 57% 4 43% 3 33% 5 67% 10 0% 0 0% 0 7% 1 93% 14 70% 14 30% 6 Burkina Faso 33% 22 67% 44 38% 23 62% 37 39% 20 61% 31 51% 23 49% 22 22% 11 78% 39 Cambodia 25% 6 75% 18 100% 2 0% 0 0% 0 100% 1 40% 4 60% 6 0% 0 100% 5 Cameroon 35% 16 65% 30 54% 19 46% 16 78% 7 22% 2 4% 2 96% 44 51% 23 49% 22 Canada 37% 40 63% 68 34% 31 66% 59 31% 21 69% 47 70% 62 30% 26 66% 29 34% 15 Cayman Islands 38% 3 63% 5 100% 12 0% 0 100% 3 0% 0 100% 4 0% 0 0% 0 0% 0 CentraLAfrican Republic 0% 0 100% 13 22% 2 78% 7 100% 10 0% 0 0% 0 0% 0 0% 0 0% 0 Chad 29% 2 71% 5 0% 0 0% 0 29% 2 71% 5 0% 0 0% 0 14% 3 86% 19 Chile 36% 13 64% 23 38% 46 62% 76 33% 20 67% 41 35% 6 65% 11 41% 28 59% 41 People's Republic of China 38% 22 62% 36 52% 39 48% 36 61% 14 39% 9 54% 77 46% 66 78% 36 22% 10 Colombia 33% 6 67% 12 23% 12 77% 40 40% 27 60% 40 0% 0 0% 0 0% 0 0% 0 Congo 67% 2 33% 1 100% 3 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 Congo (Democratic Republic of the) 0% 0 100% 14 45% 23 55% 28 47% 29 53% 33 50% 7 50% 7 31% 5 69% 11 Costa Rica 43% 29 57% 39 49% 32 51% 33 43% 18 57% 24 42% 52 58% 72 39% 23 61% 36 Cuba 59% 13 41% 9 45% 15 55% 18 59% 16 41% 11 33% 9 67% 18 58% 7 42% 5 Cyprus 42% 14 58% 19 20% 6 80% 24 0% 0 0% 0 46% 59 54% 68 53% 63 47% 55 Denmark 18% 12 82% 56 56% 32 44% 25 50% 2 50% 2 43% 9 57% 12 67% 2 33% 1 Dominica 100% 2 0% 0 0% 0 100% 12 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Dominican Republic 65% 26 35% 14 0% 0 100% 11 0% 0 100% 3 85% 22 15% 4 14% 1 86% 6 Ecuador 55% 11 45% 9 3% 1 97% 33 86% 6 14% 1 28% 26 72% 66 39% 33 61% 51 Egypt 78% 18 22% 5 41% 25 59% 36 0% 0 100% 1 22% 8 78% 29 50% 3 50% 3 El Salvador 48% 10 52% 11 27% 7 73% 19 50% 4 50% 4 21% 6 79% 22 69% 11 31% 5 Estonia 55% 11 45% 9 30% 11 70% 26 17% 2 83% 10 91% 10 9% 1 30% 7 70% 16 Eswatini 52% 48 48% 44 100% 4 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Ethiopia 22% 5 78% 18 56% 5 44% 4 33% 1 67% 2 32% 30 68% 63 32% 13 68% 28 Fiji 62% 16 38% 10 32% 8 68% 17 48% 10 52% 11 38% 5 62% 8 43% 3 57% 4 Finland 57% 59 43% 44 21% 6 79% 22 31% 4 69% 9 22% 8 78% 28 39% 7 61% 11 France 42% 37 58% 52 35% 141 65% 258 37% 31 63% 52 54% 56 46% 48 43% 18 57% 24 Gabon 33% 2 67% 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Gambia 28% 7 72% 18 0% 0 100% 2 0% 0 0% 0 0% 0 100% 6 33% 2 67% 4 Georgia 61% 17 39% 11 76% 349 24% 109 0% 0 0% 0 73% 103 27% 38 41% 11 59% 16 Ghana 19% 23 81% 100 42% 76 58% 106 15% 11 85% 64 61% 103 39% 66 54% 58 46% 50 Greenland 41% 17 59% 24 0% 0 100% 53 50% 7 50% 7 100% 1 0% 0 67% 4 33% 2 Grenada 25% 1 75% 3 50% 1 50% 1 100% 1 0% 0 69% 9 31% 4 80% 4 20% 1 Guatemala 39% 19 61% 30 45% 47 55% 57 35% 29 65% 55 47% 28 53% 32 30% 8 70% 19 Guinea 0% 0 100% 11 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 25% 1 75% 3 Guyana 33% 1 67% 2 0% 0 100% 6 0% 0 0% 0 0% 0 100% 8 38% 3 63% 5 104 APPENDIX 5-11 PRINT Reporter Reporter Reporter Female Male Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N % N % N Haiti 0% 0 100% 4 22% 11 78% 40 18% 9 82% 42 25% 1 75% 3 27% 3 73% 8 Hong Kong SAR PRC 38% 15 63% 25 35% 7 65% 13 47% 16 53% 18 79% 15 21% 4 68% 13 32% 6 Ice land 24% 5 76% 16 0% 0 100% 28 14% 3 86% 18 68% 15 32% 7 67% 12 33% 6 India 13% 7 88% 49 21% 12 79% 45 0% 0 100% 2 57% 93 43% 70 21% 5 79% 19 Indonesia 29% 10 71% 25 0% 0 0% 0 0% 0 0% 0 52% 23 48% 21 100% 1 0% 0 Iraq 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 50% 1 50% 1 0% 0 100% 3 Ireland 39% 29 61% 45 8% 3 92% 33 14% 3 86% 19 68% 27 33% 13 45% 13 55% 16 Israel 15% 7 85% 40 26% 6 74% 17 80% 4 20% 1 65% 39 35% 21 31% 10 69% 22 Italy 45% 49 55% 59 21% 9 79% 33 67% 10 33% 5 60% 33 40% 22 50% 25 50% 25 Jamaica 64% 14 36% 8 79% 34 21% 9 0% 0 0% 0 80% 35 20% 9 71% 12 29% 5 Japan 20% 9 80% 36 0% 0 0% 0 0% 0 0% 0 46% 56 54% 67 41% 9 59% 13 Jordan 78% 43 22% 12 39% 24 61% 37 0% 0 100% 1 70% 85 30% 37 20% 6 80% 24 Kenya 22% 31 78% 112 93% 14 7% 1 0% 0 0% 0 0% 0 100% 6 32% 6 68% 13 Kyrgyzstan 39% 9 61% 14 58% 21 42% 15 0% 0 0% 0 74% 28 26% 10 67% 18 33% 9 Lebanon 20% 1 80% 4 95% 19 5% 1 67% 2 33% 1 69% 11 31% 5 67% 14 33% 7 Luxembourg 25% 7 75% 21 43% 15 57% 20 0% 0 100% 3 0% 0 100% 11 36% 4 64% 7 Macao 44% 14 56% 18 56% 14 44% 11 78% 7 22% 2 9% 2 91% 20 60% 12 40% 8 Malawi 23% 10 77% 33 63% 32 37% 19 43% 13 57% 17 30% 13 70% 31 41% 9 59% 13 Malaysia 52% 22 48% 20 41% 15 59% 22 0% 0 0% 0 42% 83 58% 116 54% 13 46% 11 Mali 14% 6 86% 36 57% 8 43% 6 50% 6 50% 6 100% 9 0% 0 42% 5 58% 7 Malta 17% 17 83% 82 40% 8 60% 12 56% 5 44% 4 50% 7 50% 7 36% 15 64% 27 Mexico 44% 45 56% 58 46% 104 54% 120 48% 51 52% 55 49% 90 51% 93 39% 24 61% 38 Moldova 27% 4 73% 11 34% 20 66% 38 100% 4 0% 0 59% 86 41% 59 67% 14 33% 7 Mongolia 67% 18 33% 9 23% 7 77% 24 63% 5 38% 3 44% 24 56% 31 68% 34 32% 16 Morocco 36% 13 64% 23 43% 19 57% 25 50% 4 50% 4 16% 10 84% 51 50% 13 50% 13 Myanmar 7% 2 93% 26 100% 14 0% 0 44% 4 56% 5 76% 34 24% 11 46% 6 54% 7 Namibia 33% 12 67% 24 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 25% 1 75% 3 Nepal 20% 12 80% 47 21% 21 79% 79 46% 6 54% 7 69% 38 31% 17 16% 5 84% 27 Netherlands 29% 19 71% 47 0% 0 100% 1 50% 1 50% 1 0% 0 100% 4 30% 3 70% 7 New Zealand 59% 24 41% 17 100% 22 0% 0 100% 2 0% 0 60% 9 40% 6 60% 18 40% 12 Nicaragua 38% 5 62% 8 21% 8 79% 31 0% 0 100% 2 93% 14 7% 1 69% 18 31% 8 Niger 27% 3 73% 8 0% 0 0% 0 29% 2 71% 5 0% 0 0% 0 47% 8 53% 9 Nigeria 7% 7 93% 88 37% 13 63% 22 29% 2 71% 5 64% 42 36% 24 33% 11 67% 22 Norway 38% 40 62% 65 59% 33 41% 23 45% 14 55% 17 73% 38 27% 14 48% 20 52% 22 Pakistan 0% 0 100% 31 58% 7 42% 5 0% 0 100% 1 58% 98 42% 70 13% 5 87% 33 Palestine 30% 3 70% 7 4% 1 96% 25 67% 2 33% 1 10% 3 90% 28 23% 5 77% 17 Papua New Guinea 33% 4 67% 8 100% 3 0% 0 50% 3 50% 3 100% 12 0% 0 48% 11 52% 12 Paraguay 0% 0 100% 3 35% 15 65% 28 19% 3 81% 13 63% 39 37% 23 17% 4 83% 20 Peru 32% 10 68% 21 47% 49 53% 56 36% 12 64% 21 67% 121 33% 59 55% 67 45% 54 Poland 32% 12 68% 26 48% 68 52% 74 26% 11 74% 32 39% 54 61% 84 43% 52 57% 68 Portugal 56% 30 44% 24 16% 5 84% 27 57% 4 43% 3 30% 30 70% 69 57% 43 43% 32 Puerto Rico 73% 22 27% 8 10% 2 90% 19 40% 4 60% 6 55% 26 45% 21 39% 7 61% 11 Romania 60% 59 40% 39 72% 49 28% 19 50% 8 50% 8 57% 68 43% 52 54% 51 46% 44 Russian Federation 61% 48 39% 31 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Senegal 18% 2 82% 9 0% 0 100% 7 27% 3 73% 8 0% 0 0% 0 24% 4 76% 13 Serbia 65% 11 35% 6 67% 34 33% 17 54% 7 46% 6 62% 62 38% 38 66% 29 34% 15 Seychelles 64% 7 36% 4 0% 0 0% 0 0% 0 0% 0 100% 5 0% 0 100% 3 0% 0 Sierra Leone 33% 1 67% 2 67% 2 33% 1 100% 1 0% 0 100% 1 0% 0 0% 0 0% 0 South Africa 50% 35 50% 35 47% 7 53% 8 100% 6 0% 0 70% 21 30% 9 70% 16 30% 7 South Sudan 0% 0 100% 20 27% 4 73% 11 25% 1 75% 3 100% 13 0% 0 0% 0 100% 1 Spain 31% 25 69% 55 69% 67 31% 30 56% 45 44% 35 71% 167 29% 69 68% 96 32% 46 GMMP2020 Who Makes the News? APPENDIX 5-11 PRINT RADIO TELEVISION Reporter Presenter Reporter Presenter Reporter Female Male Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N % N % N Sirha~e 25% 1 75% 3 14% 5 86% 32 0% 0 100% 5 85% 23 15% 4 9% 1 91% 10 Sweden 43% 66 57% 89 87% 27 13% 4 47% 7 53% 8 70% 33 30% 14 51% 19 49% IS Svvitze\aid 39% 76 61% 121 54% 31 46% 26 37% 13 63% _22_ 56% 34 44% 27 32% 13 68% 2S Taiwan Province of China 47% 52 53% 58 48% 11 52% 12 0% 0 100% 1 86% 156 14% 26 55% 177 45% 143 Tanzania 41% 20 59 s: 29 48% 20 52% 22 0% 0 iC'C'% 2 67% 38 33% 19 49% 26 51% 27 Togo 13% 2 87% 13 49% 17 51% 18 8% 1 92% 12 13% 2 87% 13 57% 4 43% 3 Trinidad and Tobago 68% 17 32% 8 100% 6 0% 0 0% 0 0% 0 100% 16 0% 0 86% 6 14% 1 Tunisia 43% 18 57% 24 49% 47 51% 48 67% 6 33% 3 68% 50 32% 23 60% 27 40% 18 Turkey 30% 20 70% 46 56% 131 44% 105 8% 10 92% 115 36% 115 64% 202 16% 36 84% 192 Uganda 23% 9 77% 30 64% 21 36% 12 0% 0 0% 0 63% 24 37% 14 42% 13 58% 18 United Kingdom 36% 86 64% 153 77% 36 23% 11 28% 12 72% 31 46% 55 54% 65 50% 51 50% 50 United States of America 43» 69 57% 90 29% 6 71% 15 67% 4 33% 2 69% 31 31% 14 52% 12 48% 11 Uruguay 14% 1 86% 6 25% 50 75% 151 0% 0 100% 58 35% 57 65% 106 28% 25 72% 65 Venezuela 50% 2 50% 2 59% 58 41% 40 39% 9 61% 14 49% 44 51% 45 45% 24 55% 29 Vietnam 47% 8 53% 9 29% 4 71% 10 0% 0 100% 3 27% 3 73% 8 0% 0 100% 10 Zambia 50% 4 50% 4 50% 1 50% 1 0% 0 100% 2 100% 2 0% 0 40% 2 60% 3 Zimbabwe 23% 15 77% 51 100% 7 0% 0 0% 0 0% 0 0% 0 100% 13 18% 2 82% 9 GMMP2020 12. Reporters in print, teLevision and radio news, by major topic areas APPENDIX 5-12 I POLITICS AND GOVERNMENT ECONOMY SCIENCE AND HEALTH SOCIALAND LEGAL CRIMEAND VIOLENCE GENDER & RELATED CELEBRITY.ARTS AND MEDIA, OTHER SPORTS Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N Antigua and Barbuda 0% 0 0% 0 100% 1 0% 0 100% 1 0% 0 100% 3 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Argentina 60% 9 40% 6 42% 15 58% 21 40% 4 60% 6 55% 11 45% 9 50% 6 50% 6 0% 0 100% 1 45% 5 55% 6 0% 0 0% 0 Australia 55% 21 45% 17 45% 25 55% 30 54% 14 46% 12 44% 20 56% 25 57% 17 43% 13 0% 0 100% 2 23% 10 77% 33 0% 0 100% 2 Austria 45% 5 55% 6 33% 5 67% 10 33% 2 67% 4 100% 5 0% 0 30% 3 70% 7 0% 0 0% 0 25% 1 75% 3 0% 0 100% 1 Bangladesh 10% 1 90% 9 16% 3 84% 16 22% 2 78% 7 6% 1 94% 16 12% 3 88% 23 0% 0 100% 5 0% 0 100% 6 0% 0 0% 0 Belgium 57% 8 43% 6 55% 6 45% 5 33% 5 67% 10 57% 4 43% 3 40% 2 60% 3 50% 1 50% 1 27% 3 73% 8 0% 0 100% 1 Benin 0% 0 100% 1 50% 1 50% 1 50% 1 50% 1 100% 2 0% ° 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 13% 5 87% 33 Bolivia 48% 20 52% 22 43% 22 57% 29 42% 20 58% 28 36% 16 64% 28 30% 8 70% 19 64% 7 36% 4 45% 10 55% 12 50% 4 50% 4 Bosnia and Herzegovina 36% 9 64% 16 35% 6 65% 11 55% 11 45% 9 100% 6 0% 0 45% 5 55% 0% 0 0% 0 50% 5 50% 5 0% 0 0% 0 Botswana 33% 3 67% 6 0% 0 100% 6 0% 0 0% 0 50% 3 50% 3 71% 5 29% 2 100% 2 0% 0 0% 0 0% 0 0% 0 100% 2 Brazil 34% 15 66% 29 51% 26 49% 25 50% 17 50% 17 52% 17 48% 16 52% 16 48% 15 50% 1 50% 1 25% 2 75% 6 50% 2 50% 2 Bulgaria 63% 5 38% 3 50% 1 50% 1 75% 3 25% 1 86% 6 14% 1 50% 2 50% 2 50% 1 50% 1 0% 0 0% 0 0% 0 0% 0 Burkina Faso 19% 11 81% 47 24% 4 76% 13 52% 17 48% 16 37% 14 63% 24 50% 1 50% 1 100% 2 0% 0 24% 4 76% 13 0% 0 0% 0 Cambodia 0% 0 100% 9 50% 3 50% 3 20% 1 80% 4 17% 1 83% 5 25% 1 75% 3 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Cameroon 29% 4 71% 10 50% 14 50% 14 60% 6 40% 4 67% 20 33% 10 0% 0 0% 0 0% 0 0% 0 15% 2 85% 11 0% 0 100% 5 Canada 41% 18 59% 26 22% 10 78% 35 57% 42 43% 32 35% 6 65% 11 13% 2 88% 14 0% 0 0% 0 52% 11 48% 10 33% 1 67% 2 Cayman Islands 0% 0 0% 0 0% 0 0% 0 50% 3 50% 3 50% 1 50% 1 100% 1 0% 0 0% 0 0% 0 50% 1 50% 1 0% 0 0% 0 CentraL African Republic 35% 7 65% 13 0% 0 0% 0 100% 1 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 Chad 8% 1 92% 11 0% 0 100% 5 14% 1 86% 6 43% 3 57% 4 0% 0 100% 1 100% 2 0% 0 0% 0 100% 2 0% 0 0% 0 Chile 63% 12 37% 7 32% 11 68% 23 33% 4 67% 8 39% 17 61% 27 28% 8 72% 21 100% 1 0% 0 30% 8 70% 19 0% 0 0% 0 People's Republic of China 50% 7 50% 7 63% 25 38% 15 74% 14 26% 5 46% 18 54% 21 60% 3 40% 2 0% 0 0% 0 50% 5 50% 5 0% 0 0% 0 Colombia 28% 5 72% 13 33% 6 67% 12 70% 7 30% 3 33% 4 67% 8 14% 1 86% 6 20% 1 80% 4 60% 9 40% 6 0% 0 0% 0 Congo 50% 1 50% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 Congo (Democratic Republic of the) 18% 4 82% 18 100% 3 0% 0 40% 2 60% 3 67% 6 33% 3 0% 0 100% 1 0% 0 0% 0 100% 1 0% 0 35% 18 65% 33 Costa Rica 50% 8 50% 8 35% 17 65% 32 67% 18 33% 9 43% 12 57% 16 38% 12 63% 20 33% 3 67% 6 0% 0 100% 6 0% 0 100% 2 Cuba 100% 10 0% 0 50% 11 50% 11 61% 11 39% 7 38% 3 63% 5 0% 0 100% 1 0% 0 0% 0 50% 1 50% 1 0% 0 0% 0 Cyprus 33% 16 67% 32 50% 8 50% 8 64% 23 36% 13 67% 6 33% 3 53% 16 47% 14 0% 0 100% 1 71% 5 29% 2 75% 3 25% 1 Denmark 18% 4 82% 18 30% 3 70% 38% 5 62% 8 15% 2 85% 11 0% 0 100% 12 25% 1 75% 3 100% 1 0% 0 0% 0 0% 0 Dominica 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Dominican Republic 75% 3 25% 1 40% 6 60% 9 86% 6 14% 1 54% 7 46% 6 0% 0 100% 2 0% 0 0% 0 50% 1 50% 1 57% 4 43% 3 Ecuador 38% 5 62% 8 65% 11 35% 6 65% 11 35% 6 20% 1 80% 4 50% 18 50% 18 0% 0 0% 0 17% 4 83% 19 0% 0 0% 0 Egypt 70% 7 30% 3 60% 3 40% 2 75% 6 25% 2 83% 5 17% 1 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 El Salvador 75% 6 25% 2 57% 8 43% 6 50% 2 50% 2 89% 8 11% 1 0% 0 100% 6 33% 1 67% 2 0% 0 100% 1 0% 0 0% 0 Estonia 25% 1 75% 3 35% 9 65% 17 50% 2 50% 2 33% 3 67% 6 67% 2 33% 1 0% 0 0% 0 33% 3 67% 6 0% 0 0% 0 Eswatini 25% 3 75% 9 36% 4 64% 7 33% 1 67% 2 63% 20 38% 12 21% 3 79% 11 0% 0 0% 0 85% 17 15% 3 0% 0 0% 0 Ethiopia 18% 2 82% 9 38% 8 62% 13 14% 1 86% 6 33% 4 67% 0% 0 100% 0% 0 0% 0 33% 2 67% 4 25% 2 75% 6 Fiji 44% 4 56% 5 43% 6 57% 8 100% 4 0% 0 92% 12 8% 1 0% 0 100% 4 0% 0 0% 0 33% 2 67% 4 25% 1 75% 3 Finland 36% 13 64% 23 62% 16 38% 10 74% 17 26% 6 55% 16 45% 13 42% 5 58% 7 0% 0 0% 0 38% 3 63% 5 0% 0 0% 0 France 35% 21 65% 39 49% 22 51% 23 46% 17 54% 20 27% 6 73% 16 32% 7 68% 15 100% 2 0% 0 39% 9 61% 14 67% 2 33% 1 Gabon 0% 0 100% 1 50% 1 50% 1 0% 0 0% 0 50% 1 50% 1 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Gambia 17% 1 83% 5 29% 2 71% 5 33% 1 67% 2 40% 4 60% 6 0% 0 100% 2 0% 0 0% 0 0% 0 100% 1 50% 1 50% 1 Georgia 48% 15 52% 16 50% 2 50% 2 83% 5 17% 1 60% 3 40% 2 25% 2 75% 6 0% 0 0% o 100% 1 0% 0 0% 0 0% 0 Ghana 16% 17 84% 92 63% 19 37% 11 39% 15 61% 23 28% 29 72% 73 10% 1 90% 9 0% 0 0% 0 65% 11 35% 6 0% 0 0% 0 Greenland 45% 5 55% 6 36% 5 64% 5 35% 6 65% 11 63% 5 38% 50% 3 50% 3 100% 2 0% 0 67% 2 33% 1 0% 0 0% 0 Grenada 100% 2 0% 0 50% 1 50% 1 0% 0 100% 1 75% 3 25% 1 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Guatemala 25% 13 75% 38 38% 8 62% 13 52% 13 48% 12 36% 8 64% 14 34% 12 66% 23 0% 0 100% 2 50% 2 50% 2 0% 0 0% 0 Guinea 0% 0 100% 10 0% 0 100% 2 0% 0 100% 1 50% 1 50% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Guyana 0% 0 0% 0 33% 3 67% 6 100% 1 0% 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 107 APPENDIX 5-12 POLITICS AND GOVERNMENT ECONOMY SCIENCE AND HEALTH SOCIALAND LEGAL CRIMEAND VIOLENCE GENDER & RELATED CELEBRITY.ARTS AND MEDIA, OTHER SPORTS Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N Haiti 8% 2 92% 22 10% 1 90% 9 14% 1 86% 6 38% 5 62% 8 29% 2 71% 5 0% 0 0% 0 25% 1 75% 3 0% 0 100% 1 Hong Kong SAR PRC 51% 21 49% 20 29% 2 71% 5 71% 5 29% 2 52% 11 48% 10 36% 4 64% 7 0% 0 0% 0 17% 1 83% 5 0% 0 0% 0 Iceland 25% 1 75% 3 45% 10 55% 12 50% 6 50% 6 38% 3 63% 5 0% 0 100% 4 0% 0 0% 0 0% 0 100% 10 0% 0 0% 0 India 15% 4 85% 22 14% 3 86% 18 27% 3 73% 8 17% 2 83% 10 0% 0 100% 5 0% 0 100% 4 0% 0 100% 3 0% 0 0% 0 Indonesia 38% 3 63% 5 27% 3 73% 8 50% 2 50% 2 20% 1 80% 4 0% 0 100% 4 0% 0 0% 0 50% 1 50% 1 50% 1 50% 1 Irag 25% 1 75% 3 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Ireland 33% 8 67% 16 32% 8 68% 17 41% 14 59% 20 33% 4 67% 8 13% 2 87% 13 0% 0 0% 0 64% 9 36% 5 0% 0 100% 1 Israel 40% 8 60% 12 33% 1 67% 2 18% 7 82% 32 33% 5 67% 10 0% 0 100% 5 0% 0 0% 0 0% 0 0% 0 0% 0 100% 2 Italy 47% 17 53% 19 47% 15 53% 17 59% 19 41% 13 55% 11 45% 9 35% 9 65% 17 0% 0 0% 0 50% 13 50% 13 0% 0 100% 1 Jamaica 71% 5 29% 2 75% 6 25% 2 44% 4 56% 5 73% 8 27% 3 100% 2 0% 0 0% 0 0% 0 50% 1 50% 1 0% 0 0% 0 Japan 6% 1 94% 17 45% 10 55% 12 33% 1 67% 2 0% 0 100% 5 36% 4 64% 7 0% 0 0% 0 25% 2 75% 6 0% 0 0% 0 Jordan 68% 15 32% 7 58% 7 42% 5 52% 11 48% 10 80% 12 20% 3 30% 3 70% 7 0% 0 0% 0 17% 1 83% 5 0% 0 0% 0 Kenya 8% 5 92% 54 31% 8 69% 18 20% 3 80% 12 43% 20 57% 27 8% 1 92% 11 0% 0 0% 0 0% 0 100% 2 0% 0 100% 1 Kyrgyzstan 43% 6 57% 8 18% 2 82% 9 78% 14 22% 4 100% 3 0% 0 33% 1 67% 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 Lebanon 64% 7 36% 4 22% 2 78% 7 100% 1 0% 0 80% 4 20% 1 100% 2 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 Luxembourg 20% 3 80% 12 20% 1 80% 4 40% 2 60% 3 38% 3 63% 5 0% 0 100% 6 100% 1 0% 0 50% 1 50% 1 0% 0 0% 0 Macao 29% 2 71% 5 43% 6 57% 8 53% 8 47% 7 67% 10 33% 5 86% 6 14% 1 0% 0 0% 0 33% 1 67% 2 0% 0 0% 0 Malawi 100% 1 0% 0 17% 1 83% 5 100% 1 0% 0 38% 3 63% 5 0% 0 100% 2 100% 1 0% 0 0% 0 100% 7 36% 25 64% 44 Malaysia 36% 5 64% 9 50% 6 50% 6 57% 8 43% 6 45% 5 55% 6 73% 8 27% 3 0% 0 0% 0 67% 2 33% 1 100% 1 0% 0 Mali 15% 5 85% 29 50% 4 50% 4 60% 3 40% 2 0% 0 100% 8 50% 3 50% 3 0% 0 0% 0 0% 0 100% 2 67% 2 33% 1 Malta 29% 10 71% 24 25% 2 75% 6 15% 2 85% 11 32% 15 68% 32 19% 3 81% 13 0% 0 0% 0 8% 2 92% 24 50% 3 50% 3 Mexico 45% 19 55% 23 43% 16 57% 21 43% 16 57% 21 51% 44 49% 43 9% 2 91% 20 43% 6 57% 8 53% 16 47% 14 50% 1 50% 1 Moldova 33% 3 67% 6 83% 5 17% 1 80% 4 20% 1 78% 7 22% 2 0% 0 100% 5 0% 0 0% 0 60% 3 40% 2 0% 0 100% 1 Mongolia 80% 8 20% 2 58% 15 42% 11 67% 10 33% 5 78% 18 22% 5 60% 3 40% 2 0% 0 0% 0 50% 3 50% 3 0% 0 0% 0 Morocco 35% 6 65% 11 42% 5 58% 7 63% 10 38% 6 55% 6 45% 5 13% 1 88% 7 0% 0 0% 0 33% 2 67% 0% 0 0% 0 Myanmar 33% 1 67% 2 9% 1 91% 10 31% 8 69% 18 33% 1 67% 2 14% 1 86% 6 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Namibia 22% 2 78% 7 40% 4 60% 6 100% 1 0% 0 44% 4 56% 67% 2 33% 1 0% 0 0% o 0% 0 100% 8 0% 0 0% 0 Nepal 18% 4 82% 18 18% 5 82% 23 26% 5 74% 14 23% 6 77% 20 67% 2 33% 1 25% 1 75% 3 0% 0 100% 1 0% 0 100% 1 Netherlands 22% 6 78% 21 29% 5 71% 12 29% 2 71% 5 50% 4 50% 4 63% 5 38% 3 0% 0 0% 0 9% 1 91% 10 0% 0 0% 0 New Zealand 57% 12 43% 9 79% 11 21% 3 64% 7 36% 4 64% 7 36% 4 71% 5 29% 2 0% 0 0% 0 22% 2 78% 7 0% 0 0% 0 Nicaragua 33% 1 67% 100% 1 0% 0 100% 1 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 54% 19 46% 16 Niger 24% 5 76% 16 50% 2 50% 2 100% 1 0% 0 57% 4 43% 3 0% 0 0% 0 100% 1 0% 0 0% 0 100% 1 0% 0 0% 0 Nigeria 19% 6 81% 25 13% 6 87% 40 33% 3 67% 6 12% 3 88% 23 10% 2 90% 18 0% 0 100% 1 0% 0 100% 2 0% 0 0% 0 Norway 37% 14 63% 24 46% 19 54% 22 50% 19 50% 19 67% 12 33% 6 31% 5 69% 11 0% 0 0% 0 19% 5 81% 22 0% 0 0% 0 Pakistan 6% 2 94% 31 0% 0 100% 6 0% 0 100% 5 0% 0 100% 6 10% 1 90% 9 25% 1 75% 3 17% 1 83% 5 0% 0 0% 0 Palestine 57% 4 43% 3 33% 3 67% 6 33% 1 67% 2 0% 0 100% 2 14% 2 86% 12 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Papua New Guinea 29% 2 71% 20% 1 80% 4 71% 5 29% 2 47% 9 53% 10 0% 0 100% 0% 0 0% 0 0% 0 0% ° 100% 1 0% 0 Paraguay 0% 0 0% 0 13% 1 88% 7 20% 2 80% 8 0% 0 100% 13 25% 2 75% 6 0% 0 0% 0 0% 0 100% 1 67% 2 33% 1 Peru 40% 8 60% 12 46% 16 54% 19 64% 23 36% 13 54% 13 46% 11 49% 20 51% 21 0% 0 100% 5 38% 9 63% 15 0% 0 0% 0 Poland 34% 20 66% 38 36% 10 64% 18 39% 16 61% 25 50% 14 50% 14 30% 13 70% 30 0% 0 0% 0 67% 2 33% 1 0% 0 0% 0 Portugal 52% 13 48% 12 77% 20 23% 6 60% 18 40% 12 74% 14 26% 5 47% 8 53% 9 0% 0 0% 0 21% 4 79% 15 0% 0 0% 0 Puerto Rico 79% 11 21% 3 50% 1 50% 1 60% 6 40% 4 43% 3 57% 4 44% 4 56% 5 33% 2 67% 4 83% 5 17% 1 25% 1 75% 3 Romania 51% 44 49% 42 29% 6 71% 15 59% 16 41% 11 56% 10 44% 8 75% 9 25% 3 0% 0 100% 1 75% 33 25% 11 0% 0 0% 0 Russian Federation 45% 10 55% 12 73% 19 27% 7 80% 4 20% 1 65% 11 35% 6 100% 1 0% 0 0% 0 0% 0 38% 3 63% 5 0% 0 0% 0 Senegal 40% 2 60% 3 0% 0 100% 8 25% 1 75% 3 24% 4 76% 13 100% 2 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 100% 3 Serbia 56% 5 44% 4 45% 5 55% 6 100% 13 0% 0 64% 7 36% 4 47% 8 53% 9 100% 2 0% 0 56% 5 44% 4 100% 2 0% 0 Seychelles 80% 4 20% 1 100% 2 0% 0 0% 0 0% 0 100% 3 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 33% 1 67% 2 Sierra Leone 50% 2 50% 2 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 South Africa 78% 7 22% 2 38% 6 63% 10 78% 7 22% 2 38% 5 62% 8 60% 12 40% 8 43% 3 57% 4 71% 15 29% 6 50% 2 50% 2 South Sudan 0% 0 100% 4 0% 0 100% 7 25% 1 75% 3 0% 0 100% 7 0% 0 100% 3 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Spain 39% 27 61% 42 62% 26 38% 16 65% 44 35% 24 63% 15 38% 9 53% 17 47% 15 67% 2 33% 1 42% 8 58% 11 60% 27 40% 18 GMMP2020 108 Who Makes the News? (p^ APPENDIX 5-12 POLITICS AND GOVERNMENT ECONOMY SCIENCE AND HEALTH SOCIALAND LEGAL CRIMEAND VIOLENCE GENDER & RELATED CELEBRITY.ARTS AND MEDIA, OTHER SPORTS Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N Suriname 0% 0 100% 3 0% 0 100% 11 50% 1 50% 1 33% 1 67% 2 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 Sweden 45% 13 55% 16 45% 20 55% 24 48% 15 52% 16 49% 26 51% 27 28% 7 72% 18 100% 2 0% 0 45% 9 55% 11 0% 0 100% 3 Switzerland 42% 38 58% 52 28% 17 72% 43 34% 11 66% 21 45% 15 55% 18 31% 4 69% 9 100% 2 0% 0 36% 15 64% 27 0% 0 100% 1 Taiwan Province of China 43% 64 57% 86 57% 60 43% 45 64% 29 36% 16 60% 33 40% 22 51% 24 49% 23 100% 1 0% 0 63% 17 37% 10 100% 1 0% 0 Tanzania 41% 11 59% 16 57% 13 43% 10 33% 2 67% 4 48% 11 52% 12 100% 3 0% 0 0% 0 0% 0 36% 5 64% 9 13% 1 88% 7 Togo 0% 0 100% 10 0% 0 100% 4 100% 2 0% 0 40% 2 60% 0% 0 0% o 0% 0 0% 0 0% 0 100% 1 23% 3 77% 10 Trinidad and Tobago 86% 6 14% 1 83% 5 17% 1 100% 2 0% 0 67% 2 33% 1 33% 1 67% 2 0% 0 0% 0 78% 7 22% 2 0% 0 100% 2 Tunisia 38% 8 62% 13 31% 4 69% 9 85% 22 15% 4 33% 4 67% 8 50% 4 50% 4 0% 0 0% ° 56% 9 44% 7 0% 0 0% 0 Turkey 8% 4 92% 45 24% 8 76% 26 18% 12 82% 54 16% 15 84% 79 12% 14 88% 100 0% 0 100% 6 26% 6 74% 17 21% 7 79% 26 Uganda 28% 8 72% 21 36% 5 64% 9 20% 1 80% 4 47% 7 53% 8 0% 0 100% 4 0% 0 0% 0 33% 1 67% 2 0% 0 0% 0 United Kingdom 54% 7 46% 6 26% 5 74% 14 33% 6 67% 12 44% 4 56% 5 60% 9 40% 6 100% 1 0% 0 53% 8 47% 7 60% 3 40% 2 United States of America 45% 33 55% 41 48% 10 52% 11 32% 9 68% 19 50% 21 50% 21 54% 7 46% 6 100% 1 0% 0 44% 4 56% 5 0% 0 0% 0 Uruguay 20% 9 80% 36 40% 4 60% 6 11% 2 89% 17 27% 6 73% 16 14% 5 86% 31 0% 0 0% 0 0% 0 100% 23 0% 0 0% 0 Venezuela 58% 15 42% 11 50% 8 50% 8 47% 8 53% 9 15% 2 85% 11 0% 0 100% 2 0% 0 0% 0 67% 2 33% 1 0% 0 100% 3 Vietnam 0% 0 100% 6 25% 1 75% 3 0% 0 100% 5 55% 6 45% 5 33% 1 67% 2 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 Zambia 50% 2 50% 2 0% 0 0% 0 0% 0 0% 0 33% 1 67% 2 50% 2 50% 2 50% 1 50% 1 0% 0 100% 1 0% 0 100% 1 Zimbabwe 13% 1 88% 7 29% 5 71% 12 67% 4 33% 2 17% 4 83% 20 0% 0 100% 14 50% 1 50% 1 33% 2 67% 4 0% 0 0% 0 GMMP2020 109 Who Makes the News? (p^ 13. Subject and source selection by sex, by sex of reporter in print, television and radio stories SEX OF REPORTER FEMALE MALE SEX OF SUBJECT/SOURCE Female % N % Male N Female % N % Male N Antigua and Barbuda 0% 0 100% 7 0% 0 0% 0 Argentina 25% 97 75% 285 22% 125 78% 437 Australia 33% 246 67% 504 28% 204 72% 516 Austria 26% 21 74% 61 25% 18 75% 53 Bangladesh 16% 19 84% 100 9% 28 91% 288 Belgium 27% 79 73% 213 26% 102 75% 298 Benin 35% 18 65% 33 27% 25 73% 66 Bolivia 29% 203 71% 509 25% 106 75% 325 Bosnia and Herzegovina 18% 60 82% 269 16% 21 84% 107 Botswana 33% 4 67% 8 27% 4 73% 11 Brazil 29% 187 71% 454 24% 155 76% 479 Bulgaria 35% 18 65% 34 29% 12 71% 29 Burkina Faso 21% 49 79% 182 16% 65 84% 335 Cambodia 38% 5 62% 8 37% 28 63% 47 Cameroon 19% 37 81% 153 15% 45 85% 253 Canada 35% 167 65% 314 30% 127 70% 301 Cayman Islands 60% 9 40% 6 40% 4 60% 6 Central African Republic 13% 2 87% 13 4% 2 96% 55 Chad 33% 2 67% 4 31% 9 69% 20 Chile 29% 99 71% 244 22% 111 78% 390 People's Republic of China 26% 53 74% 148 28% 29 72% 75 Colombia 26% 23 74% 67 13% 19 87% 126 Congo 100% 2 0% 0 100% 1 0% 0 Congo (Democratic Republic of the) 30% 18 70% 42 23% 21 77% 69 Costa Rica 32% 60 68% 127 32% 107 68% 229 Cuba 16% 16 84% 87 20% 13 80% 52 Cyprus 18% 60 82% 267 21% 105 79% 386 Denmark 30% 14 70% 33 35% 45 65% 85 Dominica 50% 1 50% 1 0% 0 0% 0 Dominican Republic 23% 22 77% 72 29% 18 71% 44 Ecuador 29% 53 71% 130 24% 81 76% 261 Egypt 31% 11 69% 25 30% 6 70% 14 El Salvador 25% 20 75% 59 13% 7 87% 45 Estonia 32% 12 68% 26 26% 15 74% 43 Eswatini 20% 8 80% 33 33% 11 67% 22 Ethiopia 14% 10 86% 61 12% 16 88% 118 Fiji 29% 21 71% 21% 12 79% 46 Finland 35% 75 65% 139 33% 92 67% 183 France 29% 373 71% 928 31% 444 69% 995 Gabon 100% 1 0% 0 50% 2 50% 2 Gambia 13% 2 87% 13 13% 5 87% 34 Georgia 33% 17 67% 35 37% 26 63% 44 GMMP2020 110 Who Makes the News? SEX OF REPORTER FEMALE MALE SEX OF SUBJECT/SOURCE Female % N % Male N Female % N % Male N Ghana 22% 108 78% 383 18% 122 82% 573 Greenland 42% 30 58% 41 28% 34 72% 87 Grenada 17% 2 83% 10 50% 3 50% 3 Guatemala 30% 38 70% 89 22% 59 78% 205 Guinea 67% 2 33% 1 17% 3 83% 15 Guyana 0% 0 100% 5 0% 0 100% 16 Haiti 9% 9 91% 87 17% 34 83% 168 Hong Kong SAR PRC 33% 28 67% 56 20% 24 80% 94 Iceland 46% 26 54% 30 32% 29 68% 61 India 21% 14 79% 54 10% 26 90% 229 Indonesia 14% 17 86% 102 12% 12 88% 91 Iraq 0% 0 0% 0 0% 0 100% 3 Ireland 32% 40 68% 84 23% 47 77% 155 Israel 12% 25 88% 183 12% 50 88% 354 Italy 22% 68 78% 246 24% 74 76% 237 Jamaica 37% 33 63% 57 23% 10 77% 33 Japan 27% 84 73% 222 18% 56 82% 262 Jordan 17% 26 83% 131 12% 72 88% 539 Kenya 31% 41 69% 90 23% 180 77% 613 Kyrgyzstan 33% 18 67% 37 25% 17 75% 51 Lebanon 17% 8 83% 38 7% 2 93% 26 Luxembourg 27% 7 73% 19 25% 29 75% 88 Macao 36% 31 64% 54 27% 26 73% 69 Malawi 24% 25 76% 78 22% 42 78% 150 Malaysia 23% 22 77% 72 14% 20 86% 118 Mali 21% 15 79% 57 5% 6 95% 121 Malta 40% 66 60% 97 15% 91 85% 532 Mexico 44% 208 56% 268 35% 154 65% 289 Moldova 44% 53 56% 68 40% 33 60% 50 Mongolia 25% 47 75% 141 30% 42 70% 96 Morocco 19% 18 81% 76 19% 28 81% 123 Myanmar 9% 3 91% 31 20% 13 80% 53 Namibia 47% 17 53% 19 37% 20 63% 34 Nepal 24% 29 76% 94 19% 65 81% 283 Netherlands 24% 26 76% 83 30% 100 70% 234 New Zealand 38% 47 62% 77 26% 21 74% 59 Nicaragua 36% 8 64% 14 24% 4 76% 13 Niger 60% 6 40% 4 22% 4 78% 14 Nigeria 22% 33 78% 120 11% 52 89% 431 Norway 45% 265 55% 328 36% 233 64% 411 Pakistan 26% 26 74% 75 20% 67 80% 272 Palestine 6% 1 94% 15 11% 10 89% 80 Papua New Guinea 6% 1 94% 16 8% 2 92% 22 Paraguay 20% 9 80% 35 22% 19 78% 69 Peru 37% 155 63% 261 30% 113 70% 261 Poland 26% 144 74% 404 25% 207 75% 617 GMMP2020 111 Who Makes the News? SEX OF REPORTER FEMALE MALE SEX OF SUBJECT/SOURCE Female % N % Male N Female % N % Male N Portugal 38% 112 62% 184 31% 84 69% 188 Puerto Rico 39% 69 61% 108 40% 39 60% 59 Romania 36% 374 64% 671 30% 216 70% 510 Russian Federation 44% 116 56% 146 32% 42 68% 90 Senegal 28% 9 72% 23 15% 17 85% 95 Serbia 24% 61 76% 193 18% 26 82% 122 Seychelles 19% 3 81% 13 17% 1 83% 5 Sierra Leone 100% 2 0% 0 80% 4 20% 1 South Africa 38% 55 62% 89 36% 41 64% 73 South Sudan 0% 0 100% 2 13% 6 87% 40 Spain 36% 595 64% 1064 28% 286 72% 719 Suriname 37% 7 63% 12 51% 25 49% 24 Sweden 40% 209 60% 314 27% 137 73% 372 Switzerland 30% 138 70% 319 23% 201 77% 681 Taiwan Province of China 34% 723 66% 1424 31% 553 69% 1249 Tanzania 35% 62 65% 114 25% 38 75% 112 Togo 26% 8 74% 23 38% 28 62% 46 Trinidad and Tobago 28% 22 72% 57 19% 5 81% 21 Tunisia 27% 48 73% 133 24% 35 76% 112 Turkey 24% 141 76% 440 22% 355 78% 1253 Uganda 26% 51 74% 143 20% 75 80% 309 United Kingdom 33% 170 67% 343 30% 226 70% 533 United States of America 35% 223 65% 415 27% 179 73% 480 Uruguay 32% 47 68% 100 23% 122 77% 403 Venezuela 25% 25 75% 76 17% 19 83% 92 Vietnam 29% 12 71% 30 20% 24 80% 97 Zambia 100% 6 0% 0 82% 9 18% 2 Zimbabwe 18% 11 82% 49 21% 48 79% 179 14. This story clearly challenges gender stereotypes. Responses on print, television and radio news APPENDIX 5-14 AGREE DISAGREE TOTAL APPENDIX 5-14 AGREE DISAGREE TOTAL % N % N N % N % N N Antigua and Barbuda 10% 2 90% 19 21 Bolivia 3% 14 97% 425 439 Argentina 1% 3 99% 217 220 Bosnia and Herzegovina 0% 0 100% 186 186 Australia 3% 11 97% 368 379 Botswana 19% 12 81% 52 64 Austria 0% 0 100% 64 64 Brazil 5% 13 95% 224 237 Bangladesh 0% 0 100% 229 229 Bulgaria 8% 5 92% 59 64 Belgium 2% 4 98% 196 200 Burkina Faso 1% 1 99% 163 164 Benin 0% 0 100% 67 67 Cambodia 10% 4 90% 37 41 GMMP2020 112 Who Makes the News? APPENDIX 5-14 AGREE DISAGREE TOTAL % N % N N Cameroon 5% 7 95% 125 132 Canada 1% 4 99% 311 315 Cayman Islands 3% 1 98% 39 40 Central African Republic 0% 0 100% 22 22 Chad 0% 0 100% 38 38 Chile 2% 5 98% 270 275 People's Republic of China 1% 5 99% 326 329 Colombia 1% 2 99% 138 140 Congo 0% 0 100% 8 8 Congo (Democratic Republic of the) 1% 2 99% 159 161 Costa Rica 1% 2 99% 234 236 Cuba 0% 0 100% 106 106 Cyprus 4% 8 96% 192 200 Denmark 1% 1 99% 163 164 Dominica 0% 0 100% 20 20 Dominican Republic 1% 1 99% 84 85 Ecuador 10% 19 90% 179 198 Egypt 8% 13 92% 141 154 El Salvador 2% 2 98% 97 99 Estonia 0% 0 100% 115 115 Eswatini 4% 6 96% 138 144 Ethiopia 3% 3 97% 113 116 Fiji 6% T 94% 63 67 Finland 1% l 99% 174 175 France 2% ii 98% 446 457 Gabon 7% l 93% 13 14 Gambia 0% 0 100% 34 34 Georgia 0% 1 100% 683 684 Ghana 0% 0 100% 537 537 Greenland 0% 0 100% 77 77 Grenada 0% 0 100% 44 44 Guatemala 1% 2 99% 219 221 Guinea 0% 0 100% 18 18 Guyana 0% 0 100% 29 29 Haiti 2% 2 98% 105 107 Hong Kong SAR PRC 0% 0 100% 194 194 Iceland 0% 0 100% 80 80 India 5% 22 95% 464 486 Indonesia 12% 8 88% 59 67 Iraq 22% 2 78% 7 9 Ireland 1% 1 99% 157 158 GMMP2020 APPENDIX 5-14 AGREE DISAGREE TOTAL % N % N N Israel 0% 0 100% 104 104 Italy 1% 2 99% 218 220 Jamaica 6% 7 94% 119 126 Japan 0% 0 100% 117 117 Jordan 7% 20 93% 266 286 Kenya 5% 5 95% 100 105 Kyrgyzstan 1% 2 99% 156 158 Lebanon 0% 0 100% 80 80 Luxembourg 1% 1 99% 95 96 Macao 5% 7 95% 130 137 Malawi 4% 6 96% 140 146 Malaysia 1% 2 99% 279 281 Mali 3% 4 97% 125 129 Malta 5% 10 95% 175 185 Mexico 8% 48 92% 537 585 Moldova 3% 5 97% 183 188 Mongolia 0% 0 100% 133 133 Morocco 1% 1 99% 143 144 Myanmar 4% 4 96% 92 96 Namibia 15% 6 85% 35 41 Nepal 5% 18 95% 323 341 Netherlands 1% 1 99% 120 121 New Zealand 2% 2 98% 112 114 Nicaragua 9% 9 91% 86 95 Niger 3% 1 97% 35 36 Nigeria 1% 2 99% 177 179 Norway 3% 6 97% 174 180 Pakistan 2% 5 98% 267 272 Palestine 0% 0 100% 116 116 Papua New Guinea 22% 16 78% 57 73 Paraguay 1% 2 99% 166 168 Peru 1% 3 99% 323 326 Poland 2% 5 98% 315 320 Portugal 2% 3 98% 182 185 Puerto Rico 2% 2 98% 103 105 Romania 5% 13 95% 258 271 Russian Federation 1% 1 99% 81 82 Senegal 2% 1 98% 41 42 Serbia 2% 3 98% 193 196 Seychelles 0% 0 100% 29 29 Sierra Leone 14% J_ 86% 6 7 South Africa 4% 5 96% 128 133 Who Makes the News? APPENDIX 5-14 AGREE DISAGREE TOTAL % N % N N South Sudan 0% 0 100% 48 48 Spain 2% 7 98% 389 396 Suriname 5% 4 95% 74 78 Sweden 5% 11 95% 203 214 Switzerland 6% 21 94% 305 326 Taiwan Province of China 1% 4 99% 278 282 Tanzania 8% 16 92% 174 190 Togo 6% 5 94% 84 89 Trinidad and Tobago 0% 0 100% 59 59 Tunisia 3% 7 97% 245 252 APPENDIX 5-14 AGREE DISAGREE TOTAL % N % N N Turkey 3% 22 97% 659 681 Uganda 0% 0 100% 114 114 United Kingdom 5% 24 95% 456 480 United States of America 5% 9 95% 179 188 Uruguay 1% 5 99% 357 362 Venezuela 3% 8 97% 254 262 Vietnam 20% 9 80% 46 Zambia 36% 9 64% 16 25 Zimbabwe 1% 1 99% 97 98 15. This story clearly highlights issues of on print, television and radio news APPENDIX 5-15 AGREE DISAGREE TOTAL % N % N N Antigua and Barbuda 29% 6 71% 15 21 Argentina 2% 5 98% 215 220 Australia 2% 8 98% 371 379 Austria 0% 0 100% 64 64 Bangladesh 0% 0 100% 229 229 Belgium 1% 1 100% 199 200 Benin 3% 2 97% 65 67 Bolivia 7% 32 93% 407 439 Bosnia and Herzegovina 1% J_ 99% 184 186 Botswana 14% 9 86% 55 64 Brazil 2% 4 98% 233 237 Bulgaria 5% 3 95% 61 64 Burkina Faso 1% 2 99% 162 164 Cambodia 17% 7 83% 34 41 Cameroon 8% 11 92% 121 132 Canada 4% 13 96% 302 315 Cayman Islands 0% 0 100% 40 40 Central African Republic 0% 0 100% 22 22 Chad 5% 2 95% 36 38 Chile 4% 11 96% 264 275 People's Republic of China 0% 0 100% 329 329 GMMP2020 gender equality or inequality. Responses APPENDIX 5-15 AGREE DISAGREE TOTAL % N % N N Colombia 2% 3 98% 137 140 Congo 0% 0 100% 8 8 Congo (Democratic Republic of the) 2% 3 98% 158 161 Costa Rica 6% 15 94% 221 236 Cuba 0% 0 100% 106 106 Cyprus 4% 7 97% 193 200 Denmark 4% 7 96% 157 164 Dominica 0% 0 100% 20 20 Dominican Republic 0% 0 100% 85 85 Ecuador 3% 5 97% 193 198 Egypt 3% 4 97% 150 154 El Salvador 7% 7 93% 92 99 Estonia 0% 0 100% 115 115 Eswatini 3% 5 97% 139 144 Ethiopia 3% 4 97% 112 116 Fiji 1% 1 99% 66 67 Finland 2% 3 98% 172 175 France 3% 14 97% 443 457 Gabon 21% 3 79% 11 14 Gambia 21% 7 79% 27 34 Georgia 0% 3 100% 681 684 114 APPENDIX 5-15 AGREE DISAGREE TOTAL % N % N N Ghana 1% 4 99% 533 537 Greenland 3% 2 97% 75 77 Grenada 0% 0 100% 44 44 Guatemala 3% T 97% 215 221 Guinea 0% 0 100% 18 18 Guyana 0% 0 100% 29 29 Haiti 3% 3 97% 104 107 Hong Kong SAR PRC 0% 0 100% 194 194 Iceland 3% 2 98% 78 80 India 7% 36 93% 450 486 Indonesia 10% 7 90% 60 67 Iraq 11% 1 89% 8 9 Ireland 3% 4 97% 154 158 Israel 1% 1 99% 103 104 Italy 5% 10 95% 210 220 Jamaica 2% 2 98% 124 126 Japan 2% 2 98% 115 117 Jordan 3% 8 97% 278 286 Kenya 7% T 93% 98 105 Kyrgyzstan 1% 2 99% 156 158 Lebanon 0% 0 100% 80 80 Luxembourg 1% 1 99% 95 96 Macao 0% 0 100% 137 137 Malawi 3% 4 97% 142 146 Malaysia 1% 2 99% 279 281 Mali 3% 4 97% 125 129 Malta 4% J_ 96% 177 185 Mexico 10% 57 90% 528 585 Moldova 1% 2 99% 186 188 Mongolia 0% 0 100% 133 133 Morocco 2% 3 98% 141 144 Myanmar 6% 6 94% 90 96 Namibia 12% 5 88% 36 41 Nepal 5% 18 95% 323 341 Netherlands 3% 4 97% 117 121 New Zealand 1% 1 99% 113 114 Nicaragua 12% 11 88% 84 95 Niger 11% 4 89% 32 36 Nigeria 1% 2 99% 177 179 Norway 7% 13 93% 167 180 Pakistan 2% 6 98% 266 272 Palestine 0% 0 100% 116 116 APPENDIX 5-15 AGREE DISAGREE TOTAL % N % N N Papua New Guinea 19% 14 81% 59 73 Paraguay 1% 1 99% 167 168 Peru 2% 5 98% 321 326 Poland 1% T 99% 317 320 Portugal 2% 4 98% 181 185 Puerto Rico 8% 8 92% 97 105 Romania 2% 5 98% 266 271 Russian Federation 1% 1 99% 81 82 Senegal 2% 1 98% 41 42 Serbia 0% 0 100% 196 196 Seychelles 0% 0 100% 29 29 Sierra Leone 43% 3 57% 4 7 South Africa 2% 2 98% 131 133 South Sudan 0% 0 100% 48 48 Spain 5% 19 95% 377 396 Suriname 1% 1 99% 77 78 Sweden 5% 10 95% 204 214 Switzerland 10% 31 90% 295 326 Taiwan Province of China 0% T 100% 282 282 Tanzania 8% 16 92% 174 190 Togo 9% 8 91% 81 89 Trinidad and Tobago 7% 4 93% 55 59 Tunisia 3% 7 97% 245 252 Turkey 2% 13 98% 668 681 Uganda 1% 1 99% 113 114 United Kingdom 3% 13 97% 467 480 United States of America 17% 32 83% 156 188 Uruguay 1% 5 99% 357 362 Venezuela 6% 17 94% 245 262 Vietnam 17% 8 83% 38 46 Zambia 24% 6 76% 19 25 Zimbabwe 1% 1 99% 97 98 GMMP2020 115 Who Makes the News? 16. This story quotes or makes reference to Legislation or policy that promotes gender equality or human rights. Responses on print, radio and television news. APPENDIX 5-16 AGREE DISAGREE TOTAL % N % N H Antigua and Barbuda 67% 14 33% 7 21 Argentina 3% 7 97% 213 220 Australia 2% 9 98% 370 379 Austria 0% 0 100% 64 64 Bangladesh 0% 1 100% 228 229 Belgium 1% _2_ 99% 198 200 Benin 1% 1 99% 66 67 Bolivia 10% 46 90% 393 439 Bosnia and Herzegovina 0% 0 100% 186 186 Botswana 28% 18 72% 46 64 Brazil 7% 16 93% 221 237 Bulgaria 11% 7 89% 57 64 Burkina Faso 2% 4 98% 160 164 Cambodia 34% 14 66% 27 41 Cameroon 14% 19 86% 113 132 Canada 5% 17 95% 298 315 Cayman Islands 5% 2 95% 38 40 Central African Republic 18% 4 82% 18 22 Chad 8% T 92% 35 38 Chile 7% 18 93% 257 275 People's Republic of China 1% J_ 99% 325 329 Colombia 10% 14 90% 126 140 Congo 88% 7 13% 1 8 Congo (Democratic Republic of the) 11% 17 89% 144 _ 161 _ Costa Rica 6% 13 94% 223 236 Cuba 1% 1 99% 105 106 Cyprus 3% 5 98% 195 200 Denmark 3% 5 97% 159 164 Dominica 20% 4 80% 16 20 Dominican Republic 2% 2 98% 83 85 Ecuador 6% 17 94% 186 198 Egypt 3% 5 97% 149 154 El Salvador 38% 38 62% 61 99 Estonia 0% 0 100% 115 115 Eswatini 3% 5 97% 139 144 Ethiopia 8% 9 92% 107 116 Fiji 22% 15 78% 52 67 GMMP2020 APPENDIX 5-16 AGREE DISAGREE TOTAL % N % N N Finland 1% 2 99% 173 175 France 4% 17 96% 440 457 Gabon 64% 9 36% 5 14 Gambia 24% 8 76% 26 34 Georgia 2% 15 98% 669 684 Ghana 16% 87 84% 450 537 Greenland 8% 6 92% 71 77 Grenada 11% 5 89% 39 44 Guatemala 5% 10 95% 211 221 Guinea 22% 4 78% 14 18 Guyana 3% 1 97% 28 29 Haiti 9% 10 91% 17 107 Hong Kong SAR PRC 0% 0 100% 194 194 Iceland 8% 6 93% 74 80 India 18% 86 82% 400 486 Indonesia 15% 10 85% 57 67 Iraq 44% 4 56% 5 9 Ireland 2% 3 98% 155 158 Israel 4% 4 96% 100 104 Italy 1% 2 99% 218 220 Jamaica 5% 6 95% 120 126 Japan 0% 0 100% 117 117 Jordan 4% 11 96% 275 286 Kenya 12% 13 88% 92 105 Kyrgyzstan 1% 2 99% 156 158 Lebanon 0% 0 100% 80 80 Luxembourg 2% 2 98% 94 96 Macao 9% 13 91% 124 137 Malawi 5% 8 95% 138 146 Malaysia 0% 1 100% 280 281 Mali 8% 10 92% 119 129 Malta 9% 16 91% 169 185 Mexico 9% 52 91% 533 585 Moldova 0% 0 100% 188 188 Mongolia 10% 13 90% 120 133 Morocco 9% 13 91% 131 144 Myanmar 0% 0 100% 96 96 Namibia 34% 14 66% 27 41 Who Makes the News? APPENDIX 5-16 AGREE DISAGREE TOTAL % N % N N Nepal 21% 70 79% 271 341 Netherlands 8% 10 92% 111 121 New Zealand 3% 3 97% 111 114 Nicaragua 6% 6 94% 89 95 Niger 22% 8 78% 28 36 Nigeria 5% 9 95% 170 179 Norway 16% 28 84% 152 180 Pakistan 3% 7 97% 265 272 Palestine 1% 1 99% 115 116 Papua New Guinea 37% 27 63% 46 73 Paraguay 1% 1 99% 167 168 Peru 2% 8 98% 318 326 Poland 4% 13 96% 307 320 Portugal 2% 3 98% 182 185 Puerto Rico 16% 17 84% 88 105 Romania 2% 5 98% 266 271 Russian Federation 0% 0 100% 82 82 Senegal 14% 6 86% 36 42 Serbia 1% 2 99% 194 196 Seychelles 3% 1 97% 28 29 Sierra Leone 57% 4 43% _J_ 7 South Africa 5% 7 95% 126 153 South Sudan 17% 8 83% 40 48 Spain 8% 31 92% 365 396 Suriname 37% 29 63% 49 78 Sweden 4% 8 96% 206 214 Switzerland 16% 52 84% 274 326 Taiwan Province of China 2% 6 98% 276 282 Tanzania 39% 75 61% 115 190 Togo 17% 15 83% 74 89 Trinidad and Tobago 10% 6 90% 53 59 Tunisia 8% 19 92% 233 252 Turkey 3% 23 97% 658 681 Uganda 4% 4 96% 110 114 United Kingdom 5% 22 95% 458 480 United States of America 27% 51 73% 137 188 Uruguay 3% 11 97% 351 362 Venezuela 2% 6 98% 256 262 Vietnam 13% 6 87% 40 46 Zambia 56% 14 44% 11 25 Zimbabwe 1% 1 99% 97 98 117 17. News websites and news media tweets. Sex of reporters and news subjects & sources INTERNET VITTER APPENDIX 5-17 Reporter Female Male Subjects & Sources Male Reporter Male Subjects & Sources Male % N % N % N % N % N % N % N % N Antigua and Barbuda 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Argentina 18% 2 82% 9 23% 64 77% 216 35% 16 65% 30 25% 65 75% 196 AustraLia 54% 61 46% 51 34% 157 66% 310 56% 49 44% 39 23% 14 77% 47 Austria 36% 12 64% 21 27% 65 73% 174 0% 0 0% 0 0% 0 0% 0 Bangladesh 0% 0 100% 3 12% 8 38% 60 0% 0 0% 0 0% 0 0% 0 Belgium 43% 15 57% 20 26% 20 74% 58 44% 7 56% 9 26% 9 74% 25 Benin 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Bolivia 33% 2 67% 4 32% 51 68% 109 27% 15 73% 41 29% 17 71": 42 Bosnia and Herzegovina 36% 4 64s: 7 31% 43 c9>: 95 83% 5 17s; 1 13% 2 37': 13 Botswana 0% 0 0 s: 0 0% 0 Gsc 0 63% 5 33 s: 3 36% 5 64 % 9 Brazil 48% 28 52% 5G 27% 68 73% 185 45% 22 55% 27 31% 15 69% 34 Bu.q.vi.l 0% 0 Gsc 0 29% 5 71% 12 40% 2 60% 3 50% 6 50% 6 Burkina Faso 45% 9 55% 11 26% 16 74% 46 0% 0 0% 0 0% 0 0% G Cambodia 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Cameroon 13% 3 88% 21 14% 6 86% 36 0% 0 100% 5 0% 0 100% 25 Canada 49% 30 51% 31 38% 112 62% 179 50% 30 50% 30 37% 26 63% 45 Cayman IsLands 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 CentraLAfrican Republic 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Chad 0% 0 0% 0 0% 0 100% 3 0% 0 100% 4 50% 2 50% 2 Chile 66% 21 34% 11 46% 58 54% 69 0% 0 0': 0 0% 0 0% 0 People's Republic of China 41% 35 55 s: 51 23% 86 77»= 288 75% 3 25% 1 32% 8 68°; 17 Colombia 50% 7 50s: 7 19% 21 Sl'= 89 69% 9 31s: 4 20% 17 S0'= 7G Congo 0% 0 100== 1 100% 1 0% 0 0% 0 Gsc 0 0% 0 0% 0 Congo (Democratic Republic of the) 14% 1 36 s: 6 0% 0 1GGS= 8 0% 0 Gsc 0 0% 0 0% 0 CnsTn Rim 54% 48 46 s: 41 38% 78 62% 126 50% 34 50?.= 34 32% 32 63% ■53 Cuba 70% 26 30% 11 34% 48 66% 92 82% 18 18% 4 18% 2 82% 9 Cyprus 36% 4 64% 7 20% 5 80% 20 0% 0 0% 0 13% 3 87% 20 Denmark 36% 21 64% 38 31% 39 69% 88 0% 0 0% 0 0% 0 0% 0 Dominica 0% 0 0% 0 33% 1 67% 2 0% 0 0% 0 0% 0 0% 0 Dominican RepubLic 12% 2 S8% 15 26% 39 74% 109 13% 1 S8% 7 44% 7 56% 9 Ecuador 40% 8 60% 12 24% 57 76% 183 13% 1 88% 7 22% 23 78% 31 Egypt 43% 3 57% 4 14% 27 86% 162 0% 0 0% 0 0% 0 0% 0 El Salvador 57% 12 43% 9 22% 13 78% 46 50% 7 50% 7 23% 8 77% 27 Estonia 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Eswatini 0% 0 0% G 0% 0 0% G 0% 0 0% G 0% 0 0% 0 Ethiopia 0% 0 0% G 25% 3 75% 9 0% 0 0% 0 0% 0 0% 0 Fm 20% 6 80% 24 23% 7 77% 24 0% 0 0% G 0% 0 0% 0 Finland 53% 55 47% 49 35% 91 65% 166 46% 36 54% 43 25% 16 75% 49 GMMP2020 INTERNET TWITTER APPENDIX 5-17 Reporter Subjects & Sources Reporter Subjects & Sources Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N France 47% 31 53% 35 34% 106 66% 204 53% 21 48% 19 39% 51 61% 79 Gabon 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Gambia 0% 0 0% 0 100% 1 0% 0 100% 1 0% 0 67% 2 33% 1 Georgia 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Ghana 10% 5 90% 45 25% 34 75% 100 40% 29 60% 43 13% 22 87% 147 Greenland 47% 8 53% 9 44% 19 56% 24 0% 0 0% 0 0% 0 0% 0 Grenada 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Guatemala 71% 51 29% 21 27% 33 73% 91 60% 38 40% 25 24% 11 76% 34 Guinea 100% 1 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 Guyana 0% 0 0% 0 31% 5 69% 11 0% 0 0% 0 0% 0 0% 0 Haiti 29% 2 71% 5 28% 20 72% 51 25% 2 75% 6 20% 8 80% 32 Hong Kong SAR PRC 55% 17 45% 14 42% 60 58% 82 0% 0 100% 1 52% 26 48% 24 Iceland 24% 19 76% 59 43% 29 57% 39 0% 0 0% 0 0% 0 0% 0 India 32% 11 68% 23 21% 69 79% 256 51% 37 49% 35 29% 30 71% 74 Indonesia 47% 7 53% 8 42% 16 58% 22 0% 0 0% 0 0% 0 0% 0 Irag 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Ireland 55% 21 45% 17 19% 20 81% 83 68% 28 32% 13 20% 9 80% 37 Israel 27% 16 73% 43 24% 46 76% 142 44% 20 56% 25 24% 10 76% 31 Italy 30% 14 70% 33 29% 96 71% 231 33% 27 67% 56 25% 32 75% 95 Jamaica 60% 9 40% 6 29% 18 71% 44 79% 11 21% 3 35% 14 65% 26 Japan 0% 0 100% 1 39% 11 61% 17 37% 7 63% 12 11% 9 89% 72 Jordan 38% 3 63% 5 15% 29 85% 160 0% 0 0% 0 0% 0 0% 0 Kenya 20% 2 80% 8 20% 12 80% 48 0% 0 0% 0 0% 0 0% 0 Kyrgyzstan 64% 9 36% 5 27% 28 73% 74 0% 0 0% 0 0% 0 0% 0 Lebanon 71% 5 29% 2 24% 29 76% 94 67% 8 33% 4 19% 17 81% 74 Luxembourg 43% 15 57% 20 31% 44 69% 97 14% 2 86% 12 26% 5 74% 14 Macao 64% 7 36% 4 35% 18 65% 34 0% 0 0% 0 0% 0 0% 0 Malawi 0% 0 100% 7 40% 10 60% 15 0% 0 0% 0 0% 0 0% 0 Malaysia 43% 15 57% 20 17% 43 83% 208 21% 5 79% 19 18% 3 82% 14 Mali 0% 0 100% 4 8% 3 92% 34 0% 0 0% 0 0% 0 100% 6 Malta 44% 85 56% 110 26% 71 74% 204 0% 0 100% 1 18% 12 82% 53 Mexico 55% 39 45% 32 31% 71 69% 155 42% 48 58% 66 38% 64 62% 105 Moldova 82% 9 18% 2 38% 38 62% 63 0% 0 0% 0 19% 21 81% 87 Mongolia 72% 18 28% 7 25% 31 75% 94 0% 0 0% 0 13% 3 88% 21 Morocco 32% 10 68% 21 18% 22 82% 98 0% 0 0% 0 0% 0 0% 0 Myanmar 18% 2 82% 9 27% 13 73% 36 0% 0 0% 0 0% 0 0% 0 Namibia 0% 0 0% 0 0% 0 0% 0 0% 0 0% o 0% 0 0% 0 Nepal 26% 6 74% 17 23% 27 77% 89 7% 1 93% 14 31% 8 69% 18 Netherlands 40% 4 60% 6 22% 20 78% 71 24% 5 76% 16 24% 10 76% 31 New Zealand 55% 17 45% 14 37% 43 63% 73 25% 1 75% 5 41% 12 59% 17 119 APPENDIX 5-17 Reporter Subjects & Sources Reporter Subjects & Sources Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N Nicaragua 0% 0 100% 9 13% 2 87% 13 0% 0 0% 0 0% 0 0% 0 Niger 73% 8 27% 3 36% 4 64% 7 0% 0 0% 0 0% 0 0% 0 Nigeria 32% 16 68% 34 22% 39 78% 139 20% 8 80% 33 29% 12 71% 29 Norway 46% 26 54% 30 25% 33 75% 98 0% 0 0% 0 13% 8 87% 52 Pakistan 25% 7 75% 21 15% 19 85% no 46% 13 54% 15 40% 17 60% 25 Palestine 0% 0 0% 0 21% 18 79% 66 0% 0 0% 0 0% 0 0% 0 Papua New Guinea 100% 4 0% 0 19% 3 81% 13 0% 0 0% 0 0% 0 0% 0 Paraguay 40% 4 60% 6 29% 18 71% 45 57% 4 43% 3 13% 6 88% 42 Peru 44% 7 56% 9 25% 18 75% 53 0% 0 0% 0 0% 0 0% 0 Poland 46% 29 54% 34 33% 43 67% 86 45% 29 55% 36 29% 16 71% 39 Portugal 72% 21 28% 8 44% 44 56% 57 65% 20 35% 11 21% 8 79% 30 Puerto Rico 54% 14 46% 12 45% 62 55% 75 67% 16 33% 8 40% 17 60% 26 Romania 20% 26 80% 103 28% 61 72% 156 40% 2 60% 3 100% 10 0% 0 Russian Federation 59% 50 41% 35 30% 75 70% 175 0% 0 0% o 0% 0 0% 0 Senegal 0% 0 100% 20 13% 9 87% 62 0% 0 0% 0 0% 0 0% 0 Serbia 67% 8 33% 4 21% 28 79% 106 61% 11 39% 7 37% 16 63% 27 Seychelles 100% 4 0% 0 20% 2 80% 8 0% 0 0% 0 0% 0 0% 0 Sierra Leone 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 South Africa 29% 5 71% 12 31% 10 69% 22 0% 0 0% 0 0% 0 0% 0 South Sudan 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Spain 40% 21 60% 32 24% 58 76% 179 42% 11 58% 15 31% 32 69% 72 Suriname 0% 0 100% 4 5% 2 95% 41 0% 0 100% 2 16% 4 84% 21 Sweden 37% 32 63% 54 34% 76 66% 148 0% 0 0% 0 0% 0 0% 0 Switzerland 33% 49 67% 98 28% 182 72% 473 22% 15 78% 53 23% 25 77% 86 Taiwan Province of China 36% 12 64% 21 41% 87 59% 127 0% 0 0% 0 0% 0 0% 0 Tanzania 50% 1 50% 1 30% 3 70% 7 6% 1 94% 15 0% 0 100% 20 Togo 0% 0 100% 7 31% 5 69% 11 0% 0 0% 0 0% 0 0% 0 Trinidad and Tobago 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 14% 1 86% 6 Tunisia 0% 0 100% 6 23% 30 77% 102 0% 0 0% 0 0% 0 0% 0 Turkey 33% 2 67% 4 21% 60 79% 227 24% 4 76% 13 21% 45 79% 167 Uganda 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 United Kingdom 38% 61 62% 99 29% 142 71% 355 46% 67 54% 79 31% 45 69% 101 United States of America 43% 24 57% 32 32% 21 68% 44 0% 0 100% 1 29% 2 71% 5 Uruguay 33% 1 67% 2 25% 26 75% 77 10% 6 90% 56 20% 22 80% 90 Venezuela 31% 5 69% 11 18% 17 82% 77 61% 25 39% 16 28% 39 72% 99 Vietnam 63% 5 38% 3 30% 8 70% 19 0% 0 0% 0 0% 0 0% 0 Zambia 0% 0 0% 0 100% 11 0% 0 0% 0 0% 0 0% 0 0% 0 Zimbabwe 43% 6 57% 8 30% 20 70% 46 0% 0 0% 0 0% 0 0% 0 GMMP2020 120 Who Makes the News? 18. News websites and news media tweets. News subjects & sources, by sex APPENDIX 5-18 INTERNET TWITTER Female Male Female Male % N % N N % N % N N Antigua and Barbuda 0% 0 0% 0 0 0% 0 0% 0 0 Argentina 23% 64 77% 216 280 25% 65 75% 196 261 Australia 34% 157 66% 310 467 23% 14 77% 47 61 Austria 27% 65 73% 174 239 0% 0 0% 0 0 Bangladesh 12% 8 88% 60 68 0% 0 0% 0 0 Belgium 26% 20 74% 58 78 26% 9 74% 25 34 Benin 0% 0 0% 0 0 0% 0 0% 0 0 Bolivia 32% 51 68% 109 160 29% 17 71% 42 59 Bosnia and Herzegovina 31% 43 69% 95 138 13% 2 87% 13 15 Botswana 0% 0 0% 0 0 36% 5 64% ~T 14 Brazil 27% 68 73% 185 253 31% 15 69% 34 49 Bulgaria 29% _J_ 71% 12 17 50% 6 50% _6_ 12 Burkina Faso 26% 16 74% 46 62 0% 0 0% 0 0 Cambodia 0% 0 0% 0 0 0% 0 0% 0 0 Cameroon 14% 6 86% 36 42 0% 0 100% 25 25 Canada 38% 112 62% 179 291 37% 26 63% 45 71 Cayman Islands 0% 0 0% 0 0 0% 0 0% 0 0 Central African Republic 0% 0 0% 0 0 0% 0 0% 0 0 Chad 0% 0 100% 3 3 50% 2 50% 2 4 Chile 46% 58 54% 69 127 0% 0 0% 0 0 People's Republic of China 23% 86 77% 288 374 32% 8 68% 17 25 Colombia 19% 21 81% 89 110 20% 17 80% 70 87 Congo 100% 1 0% 0 1 0% 0 0% 0 0 Congo (Democratic Republic of the) 0% 0 100% 8 8 0% 0 0% 0 0 Costa Rica 38% 78 62% 126 204 32% 32 68% 68 100 Cuba 34% 48 66% 92 140 18% 2 82% 9 11 Cyprus 20% 5 80% 20 25 13% 3 87% 20 23 Denmark 31% 39 69% 88 127 0% 0 0% 0 0 Dominica 33% 1 67% 2 3 0% 0 0% 0 0 Dominican Republic 26% 39 74% 109 148 44% 7 56% 9 16 Ecuador 24% 57 76% 183 240 22% 23 78% 81 104 Egypt 14% 27 86% 162 189 0% 0 0% 0 0 El Salvador 22% 13 78% 46 59 23% 8 77% 27 35 Estonia 0% 0 0% 0 0 0% 0 0% 0 0 Eswatini 0% 0 0% 0 0 0% 0 0% 0 0 Ethiopia 25% 3 75% 9 12 0% 0 0% 0 0 Fiji 23% 7 77% 24 31 0% 0 0% 0 0 Finland 35% 91 65% 166 257 25% 16 75% 49 65 GMMP2020 121 Who Makes the News? APPENDIX 5-18 INTERNET TWITTER Female Male Female Male % N % N N % N % N N France 34% 106 66% 204 310 39% 51 61% 79 130 Gabon 0% 0 0% 0 0 0% 0 0% 0 0 Gambia 100% 1 0% 0 1 67% 2 33% 1 3 Georgia 0% 0 0% 0 0 0% 0 0% 0 0 Ghana 25% 34 75% 100 134 13% 22 87% 147 169 Greenland 44% 19 56% 24 43 0% 0 0% 0 0 Grenada 0% 0 0% 0 0 0% 0 0% 0 0 Guatemala 27% 33 73% 91 124 24% 11 76% 34 45 Guinea 100% 1 0% 0 1 0% 0 0% 0 0 Guyana 31% 5 69% 11 16 0% 0 0% 0 0 Haiti 28% 20 72% 51 71 20% 8 80% 32 40 Hong Kong SAR PRC 42% 60 58% 82 142 52% 26 48% 24 50 Iceland 43% 29 57% 39 68 0% 0 0% o o India 21% 69 79% 256 325 29% 30 71% 74 104 Indonesia 42% 16 58% 22 38 0% 0 0% 0 0 Irag 0% 0 0% 0 0 0% 0 0% 0 0 Ireland 19% 20 81% 83 103 20% 9 80% 37 46 Israel 24% 46 76% 142 188 24% 10 76% 31 41 Italy 29% 96 71% 231 327 25% 32 75% 95 127 Jamaica 29% 18 71% 44 62 35% 14 65% 26 40 Japan 39% 11 61% 17 28 11% 9 89% 72 81 Jordan 15% 29 85% 160 189 0% 0 0% 0 0 Kenya 20% 12 80% 48 60 0% 0 0% 0 0 Kyrgyzstan 27% 28 73% 74 102 0% 0 0% 0 0 Lebanon 24% 29 76% 94 123 19% 17 81% 74 91 Luxembourg 31% 44 69% 97 141 26% 5 74% 14 19 Macao 35% 18 65% 34 52 0% 0 0% 0 0 Malawi 40% 10 60% 15 25 0% 0 0% 0 0 Malaysia 17% 43 83% 208 251 18% 3 82% 14 17 Mali 8% 3 92% 34 37 0% 0 100% 6 6 Malta 26% 71 74% 204 275 18% 12 82% 53 65 Mexico 31% 71 69% 155 226 38% 64 62% 105 169 Moldova 38% 38 62% 63 101 19% 21 81% 87 108 Mongolia 25% 75% 94 125 13% 3 88% 21 24 Morocco 18% 22 82% 98 120 0% 0 0% 0 0 Myanmar 27% 13 73% 36 49 0% 0 0% 0 0 Namibia 0% 0 0% 0 0 0% 0 0% 0 0 Nepal 23% 27 77% 89 116 31% 8 69% 18 26 Netherlands 22% 20 78% 71 91 24% 10 76% 31 41 New Zealand 37% 43 63% 73 116 41% 12 59% 17 29 GMMP2020 122 Who Makes the News? APPENDIX 5-18 INTERNET TWITTER Female Male Female Male % N % N N % N % N N Nicaragua 13% 2 87% 13 15 0% 0 0% 0 0 Niger 36% 4 64% 7 11 0% 0 0% 0 0 Nigeria 22% 39 78% 139 178 29% 12 71% 29 41 Norway 25% 33 75% 98 131 13% 8 87% 52 60 Pakistan 15% 19 85% 110 129 40% 17 60% 25 42 Palestine 21% 18 79% 66 84 0% 0 0% _L o Papua New Guinea 19% 3 81% 13 16 0% 0 0% 0 0 Paraguay 29% 18 71% 45 63 13% 6 88% 42 48 Peru 25% 18 75% 53 71 0% 0 0% 0 0 Poland 33% 43 67% 86 129 29% 16 71% 39 55 Portugal 44% 44 56% 57 101 21% 8 79% 30 38 Puerto Rico 45% 62 55% 75 137 40% 17 60% 26 43 Romania 28% 61 72% 156 217 100% 10 0% 0 10 Russian Federation 30% 75 70% 175 250 0% 0 0% 0 0 Senegal 13% 9 87% 62 71 0% 0 0% 0 0 Serbia 21% 28 79% 106 134 37% 16 63% 27 43 Seychelles 20% 2 80% 8 10 0% 0 0% 0 0 Sierra Leone 0% 0 0% 0 0 0% 0 0% 0 0 South Africa 31% 10 69% 22 32 0% 0 0% 0 0 South Sudan 0% 0 0% 0 0 0% 0 0% 0 0 Spain 24% 58 76% 179 237 31% 32 69% 72 104 Suriname 5% 2 95% 41 43 16% 4 84% 21 25 Sweden 34% 76 66% 148 224 0% 0 0% 0 0 Switzerland 28% 182 72% 473 655 23% 25 77% 86 111 Taiwan Province of China 41% 87 59% 127 214 0% 0 0% 0 0 Tanzania 30% 3 70% 7 10 0% 0 100% 20 20 Togo 31% _J_ 69% 11 16 0% 0 0% 0 0 Trinidad and Tobago 0% 0 0% 0 0 14% 1 86% 6 7 Tunisia 23% 30 77% 102 132 0% 0 0% 0 0 Turkey 21% 60 79% 227 287 21% 45 79% 167 212 Uganda 0% 0 0% 0 0 0% 0 0% 0 0 United Kingdom 29% 142 71% 355 497 31% 45 69% 101 146 United States of America 32% 21 68% 44 65 29% 2 71% 5 7 Uruguay 25% 26 75% 77 103 20% 22 80% 90 112 Venezuela 18% 17 82% 77 94 28% 39 72% 99 138 Vietnam 30% 8 70% 19 27 0% 0 0% 0 0 Zambia 100% 11 0% 0 11 0% 0 0% 0 0 Zimbabwe 30% 20 70% 46 66 0% 0 0% 0 0 GMMP2020 123 Who Makes the News? 19. News websites and news media tweets. News subjects & sources in major topic areas, by sex INTERNET I Crime and Vi. APPENDIX 5-20 I 1| 5 I £2 2 s'l =1 J'i ig s'g S J S if ! II H Si ii !> if !| '* V n fi Is Who Makes the News; 21. News websites - Function of subjects & sources, by sex APPENDIX 5-21 DO NOT KNOW SUBJECT SPOKESPERSON EXPERT OR COMMENTATOR PERSONAL EXPERIENCE EYEWITNESS POPULAR OPINION OTHER Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N % N X N % N % N % N % N % N % N Antigua and Barbuda 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Argentina 31% 35 69% 78 16% IS S4sc 97 18% 5 82% 23 33% 4 8 100% 1 0% 0 11% 1 S9sc 8 0% 0 0% 0 0% 0 100s: 2 Australia 100% 1 0% 0 35s: 90 65% 166 26% 20 74% 57 32% 30 6Ssc 63 30% 9 70% 21 50% 3 50% 3 100% 2 0% 0 100% 2 0% 0 Austria 0% 0 0% 0 25% 32 75% 96 27% 15 73% 41 37% 11 63% 19 20% 3 80% 12 50% 4 50% 4 0% 0 100% 1 0% 0 0% 0 Bangladesh 0% 0 100% 8 12% 4 88% 29 25% 3 75% 9 9% 1 91% 10 0% 0 100% 1 0% 0 100% 1 0% 0 0% 0 0% 0 100% 2 Belgium 0% 0 0% 0 27% 16 73% 43 50% 1 50% 1 13% 2 88% 14 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Benin 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Bolivia 50% 3 50% 3 38% 27 62% 44 23% 9 78% 31 24% 5 76% 16 50% 1 50% 1 30% 3 70% 7 0% 0 100% 1 33% 3 67% 6 Bosnia and Herzegovina 0% 0 0% 0 43% 20 57% 27 9% 2 91% 20 27% 13 73% 36 40% 8 60% 12 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Botswana 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Brazil 40% 2 60% 3 31% 40 69% 87 18% 6 82% 28 14% 4 86% 25 20% 3 80% 12 100% 1 0% 0 67% 2 33% 1 26% 10 74% 29 Bulgaria 0% 0 100% 2 100% 2 0% 0 14% 1 86% 6 40% 2 60% 3 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Burkina Faso 100% 1 0% 0 23% 10 77% 34 22% 2 78% 7 67% 2 33% 1 0% 0 0% 0 33% 1 67% 2 0% 0 0% 0 0% 0 100% 2 Cambodia 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Cameroon 0% 0 100% 2 11% 3 89% 25 22% 2 78% 7 50% 1 50% 1 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 Canada 15% 3 85% V\ 35% 20 65% 37 30% 29 70% 69 41% 30 59s: 43 62% 18 38% 111 0% 0 0% 0 80% s 20% 2 100% 4 0% 0 Cayman Islands 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0s: 0 0% 0 0% 0 0% 0 0s: 0 0% 0 0% 0 0% 0 0s: 0 Central African Republic 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Cbad 0% 0 0% 0 0% 0 100% 1 0% 0 100% 2 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Cni.e 0% 0 0% 0 42% 33 58% 45 61% 19 39% 12 25% 3 75s: 9 75% 3 25% 1 0% 0 0s: 0 0% 0 0% 0 0% 0 100s: 2 People's Republic of China 50% 2 50% 2 26% 41 74% 119 17% 22 83% 106 8% 2 92% 22 36% 17 64% 30 40% 2 60% 3 0% 0 0% 0 0% 0 0% 0 Colombia 23% 6 77% 20 7% 2 93% 26 28% 5 72% 13 33% 4 67% S 50% 1 50% 1 13% 3 87% 20 0% 0 0% 0] 0% 0 100% 1 Congo 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Congo (Democratic Republic of the) 0% 0 0% 0 0% 0 100% 6 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 Costa Rica 0% 0 100% 2 44% 43 56% 54 29% 19 71% 46 58% 7 42% 5 50% 3 50% 3 30% 3 70% 7 50% 1 50% 1 20% 2 80% 8 Cuba 100% 1 0% 0 32% 16 68% 34 27% 17 73% 45 50% 10 50% 10 0% 0 100% 2 0% 0 0% 0 75% 3 25% 1 0% 0 0% 0 Cyprus 0% 0 0% 0 38% 3 63% 5 9% 1 91% 10J 0% 0 100% 4 50% 1 50% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Denmark 0% 0 0% 0 20% 1 80% 4 37% 31 63% HI 12% 3 88% 23 33% 4 67% 8 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Dominica 0% 0 0% 0 0% 0 0% 0 33% 1 67% 2 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Dominican Republic 0% 0 0% 0 18% 8 82% 36 27% 9 73% 24 48% 11 52% 12 0% 0 0% 0 54% 7 46% 6 0% 0 0% 0 12% 4 88% 30 Ecuador 0% 0 0% 0 24% 52 76% 161 17% 3 83% lFl 25% 2 75% 6 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 Egypt 0% 0 100% 1 18% IS 32% 84 10% 7 90% 63 S3: 1 92% 11 0% 0 100% 3 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 El Salvador 0% 0 100% 2 25% 4 75% 12 22% 2 78% 7 22% 4 78% 14 0% 0 0% 0 0% 0 100% 2 0% 0 0% 0 25% 3 75% 9 Estonia 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Eswatini 0% 0 0% 0 U3: 0 0 s: 0 0% 0 0% 0 0 s: 0 0s: 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 U3: 0 Ethiopia 0% 0 0% 0 100% 1 0% 0 17% 1 83% 5 20?= 1 80% 4 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Fiji 0% 0 100% 1 11% 2 89% 16 100% 1 0% 0 44 s: 4 56% 5 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Finland 50% 1 50% 1 30s: 31 70s: 71 36% 28 64% 49 27s: 10 73s: 27 45% 9 55% 11 50% 2 50s: 2 69% 9 31% 4 50% 1 50s: 1 France 100% 2 0% 0 31% 39 69% 87 43% 22 57% 29 26% 20 74% 56 48% 11 52% 12 31% 4 69% 9 0% 0 0% 0 42% s 58% 11 130 APPENDIX 5-21 DO NOT KNOW SUBJECT SPOKESPERSON EXPERT OR COMMENTATOR PERSONAL EXPERIENCE EYEWITNESS POPULAR OPINION OTHER Female Male Female Male Female Male Female Male Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N % N Gabon 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Gambia 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Georgia 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Ghana 0% 0 0% 0 20% 5 80% 20 26% 25 74% 71 31% 4 69% 9 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Greenland 0% 0 0% 0 50% 1 50% 1 41% 15 59% 22 50% 1 50% 1 100% 2 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Grenada 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Guatemala 100% 1 0% 0 26% 25 74% 73 17% 2 83% 10 29% 2 71% 5 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 50% 3 50% 3 Guinea 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Guyana 0% 0 100% 1 30% 3 70% 7 0% 0 100% 1 0% 0 100% 2 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 100% 2 0% 0 Haiti 0% 0 0% 0 24% 11 76% 35 44% 4 56% 5 38% 3 63% 5 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 25% 2 75% 6 Hong Kong SAR PRC 0% 0 0% 0 54% 38 46% 32 32% 18 68% 38 20% 2 80% 8 33% 2 67% 4 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Iceland 0% 0 100% 1 35% 8 65% 15 57% 17 43% 13 11% 1 89% 8 60% 3 40% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 India 25% 1 75% 3 21% 52 79% 191 17% 9 83% 43 33% 3 67% 6 0% 0 100% 9 0% 0 100% 1 50% 3 50% 3 100% 1 0% 0 Indonesia 40% 2 60% 3 67% 8 33% 4 17% 2 83% 10 0% 0 0% 0 0% 0 0% 0 0% 0 100% 4 50% 1 50% 1 100% 3 0% 0 Irag 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Ireland 0% 0 0% 0 20% 5 80% 20 18% 13 82% 60 0% 0 0% 0 25% 1 75% 3 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 Israel 0% 0 100% 1 23% 29 77% 95 0% 0 100% 6 24% 10 76% 31 42% 5 58% 7 0% 0 0% 0 67% 2 33% 1 0% 0 100% 1 Italy 0% 0 100% 1 32% 65 68% 141 28% 8 72% 21 16% 9 84% 49 45% 5 55% 6 25% 1 75% 3 0% 0 0% 0 47% 7 53% 8 Jamaica 0% 0 0% 33% 8 67% 16 10% 2 90% 18 29% 2 71% 60% 6 40% 0% 0 100% 1 0% 0 0% 0% 0 0% 0 Japan 0% 0 0% 0 67% 2 33% 1 29% 5 71% 12 0% 0 100% 2 75% 3 25% 1 50% 1 50% 1 0% 0 0% 0 0% 0 0% 0 Jordan 0% 0 100% 3 21% 21 79% 81 10% 7 90% 63 9% 1 91% 10 0% 0 100% 3 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Kenya 100% 1 0% 0 22% 4 78% 14 0% 0 100% 3 13% 2 87% 13 25% 1 75% 3 0% 0 0% 0 19% 3 81% 13 33% 1 67% 2 Kyrgyzstan 0% 0 100% 2 23% 16 77% 54 35% 9 65% 17 50% 1 50% 1 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 lebanon 0% 0 0% 29% 21 71% 52 11% 3 89% 24 20% 4 80% 16 0% 0 100% 1 0% 0 0% o 0% 0 0% 0 0% 0 0% 0 luxem bourg 0% 0 0% 0 29% 12 71% 29 22% 8 78% 28 32% 12 68% 26 40% 2 60% 3 100% 4 0% 0 0% 0 0% 0 35% 6 65% 11 Macao 0% 0 0% 0 30% 6 70% 14 43% 10 57% 13 0% 0 100% 1 25% 2 75% 6 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Malawi 0% 0 0% 0 29% 2 71% 5 43% 3 57% 4 40% 4 60% 6 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 Malaysia 0% 0 0% 0 11% 16 89% 124 20% 13 80% 52 24% 5 76% 16 60% 3 40% 2 38% 3 63% 5 0% 0 0% 0 25% 3 75% 9 Mali 7% 1 93% 14 13% 2 87% 13 0% 0 100% 6 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Malta 67% 2 33% 1 19% 28 81% 116 26% 10 74% 28 30% 18 70% 43 50% 4 50% 4 0% 0 100% 1 0% 0 0% 0 50% 9 50% 9 Mexico 86% 6 14% 1 28% 47 72% 122 47% 8 53% 9 26% 6 74% 17 29% 2 71% 5 0% 0 0% 0 0% 0 0% 0 50% 1 50% 1 Moldova 0% 0 100% 2 39% 22 61% 34 38% 6 63% 10 31% 5 69% 11 100% 4 0% 0 33% 1 67% 2 0% 0 100% 2 0% 0 100% 2 Mongolia 33% 1 67% 2 27% 29 73% 80 0% 0 100% 3 0% 0 100% 7 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Morocco 5% 1 95% 18 22% 14 78% 49 5% 1 95% 18 33% 5 67% 10 0% 0 0% 0 25% 1 75% 3 0% 0 0% 0 0% 0 0% 0 Myanmar 0% 0 0% 0 50% 2 50% 2 15% 4 85% 22 30% 3 70% 7 50% 3 50% 3 33% 1 67% 2 0% 0 0% 0 0% 0 0% 0 Namibia 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Nepal 100% 2 0% 38% 14 62% 23 10% 5 90% 44 0% 0 100% 4 31% 4 69% 9 0% 0 100% 1 25% 2 75% 6 0% 0 100% 2 Netherlands 33% 1 67% 2 18% 9 82% 42 23% 3 77% 10 24% 4 76% 13 50% 1 50% 1 50% 1 50% 1 0% 0 0% 0 33% 1 67% 2 New Zealand 0% 0 0% 0 44% 12 56% 15 36% 16 64% 29 39% 7 61% 11 40% 8 60% 12 0% 0 100% 4 0% 0 0% 0 0% 0 100% 1 Nicaragua 0% 0 0% 0 0% 0 100% 5 0% 0 100% 8 100% 1 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 Niger 0% 0 0% 0 43% 3 57% 4 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 100% 2 0% 0 0% 0 0% 0 0% 0 GMMP2020 131 Who Makes the News? (\\\ APPENDIX 5-21 DO NOT KNOW SUBJECT SPOKESPERSON EXPERT OR COMMENTATOR PERSONAL EXPERIENCE EYEWITNESS POPULAR OPINION OTHER Female % N Male % N Female % N Male % N Female % N Male % N Female % N Male % N Female % N Male % N Female % N Male % N Female % N Male % N Female % N Male % N Nigeria 0% 0 100% 1 20% 25 80% 103 20% 5 80% 20 33% 1 67% 2 50% 1 50% 1 0% 0 100% 2 0% 0 0% 0 41% 7 59% 10 Norway 0% 0 100% 1 24% 9 76% 29 24% 11 76% 35 29% 5 71% 12 39% 7 61% 11 0% 0 100% 1 20% 1 80% 4 0% 0 100% 5 Pakistan 0% 0 0% 0 17% 11 83% 55 12% 3 88% 23 13% 2 87% 13 60% 3 40% 2 0% 0 100% 2 0% 0 100% 3 0% 0 100% 3 Palestine 0% 0 0% 0 24% 11 76% 34 19% 6 81% 26 20% 1 80% 4 0% 0 100% 2 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Papua New Guinea 0% 0 0% 0 50% 3 50% 3 0% 0 100% 9 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 Paraguay 14% 1 86% 6 38% 15 62% 24 13% 1 88% 7 17% 1 83% 5 0% 0 100% 3 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Peru 0% 0 100% 1 26% 13 74% 37 11% 1 89% 8 25% 1 75% 3 50% 2 50% 1 0% 0 100% 1 0% 0 0% 0 50% 1 50% 1 Poland 0% 0 0% 0 38% 21 62% 34 0% 0 100% 4 25% 13 75% 38 67% 6 33% 3 43% 3 57% 4 0% 0 0% 0 0% 0 100% 3 Portugal 11% 1 89% 8 50% 15 50% 15 46% 23 54% 27 44% 4 56% 5 50% 1 50% 1 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 Puerto Rico 100% 3 0% 0 45% 37 55% 45 52% 16 48% 15 22% 4 78% 14 100% 2 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 Romania 100% 1 0% 0 29% 47 71% 113 36% 4 64% 7 4% 1 96% 22 33% 1 67% 2 50% 1 50% 1 0% 0 100% 5 60% 6 40% 4 Russian Federation 20% 3 80% 12 29% 45 71% 112 25% 7 75% 21 32% 8 68% 17 50% 3 50% 3 41% 7 59% 10 100% 1 0% 0 100% 1 0% 0 Senegal 0% 0 0% 0 21% 7 79% 26 9% 1 91% 10 13% 1 88% 7 0% 0 100% 2 0% 0 100% 5 0% 0 0% 0 0% 0 100% 12 Serbia 0% 0 0% 0 17% 18 83% 85 22% 2 78% 17% 2 83% 10 67% 6 33% 3 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 Seychelles 0% 0 0% 0 25% 1 75% 3 0% 0 100% 3 33% 1 67% 2 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Sierra Leone 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 South Africa 0% 0 0% 0 50% 2 50% 2 27% 4 73% 11 50% 3 50% 3 0% 0 100% 2 0% 0 100% 3 0% 0 100% 1 100% 1 0% 0 South Sudan 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Spain 25% 6 75% 18 22% 37 78% 130 47% 7 53% 8 8% 1 92% 11 100% 1 0% 0% 0 0% 0 0% 0 0% o 35% 6 65% 11 Suriname 0% 0 0% 0 4% 1 96% 22 6% 1 94% 15 0% 0 100% 4 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Sweden 0% 0 0% 0 32% 19 68% 40 36% 42 64% 74 31% 10 69% 22 33% 3 67% 6 0% 0 100% 2 0% 0 0% 0 50% 2 50% 2 Switzerland 0% 0 0% 0 29% 132 71% 326 22% 10 78% 35 20% 21 80% 85 38% 12 63% 20 50% 5 50% 5 50% 2 50% 2 0% 0 0% 0 Taiwan Province of China 0% 0 0% 0 43% 59 57% 79 30% 8 70% 19 31% 5 69% 11 36% 4 64% 7 25% 1 75% 3 0% 0 100% 1 59% 10 41% 7 Tanzania 0% 0 0% 0 25% 1 75% 3 0% 0 100% 2 50% 1 50% 1 100% 1 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 Togo 0% 0 0% 0 40% 2 60% 3 33% 3 67% 6 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 Trinidad and Tobago 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Tunisia 26% 6 74% 17 22% 18 78% 63 8% 1 92% 12 13% 1 88% 7 50% 1 50% 1 100% 2 0% 0 0% 0 0% 0 0% 0 100% 1 Turkey 11% 1 89% 8 26% 47 74% 131 2% 1 98% 43 17% 7 83% 34 33% 1 67% 2 20% 1 80% 4 25% 1 75% 3 33% 1 67% 2 Uganda 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 United Kingdom 0% 0 0% 0 29% 61 71% 149 26% 29 74% 81 24% 28 76% 91 50% 19 50% 19 25% 1 75% 3 22% 2 78% 7 29% 2 71% 5 United States of America 0% 0 100% 2 67% 8 33% 4 33% 3 67% 6 13% 2 88% 14 46% 6 54% 7 0% 0 100% 4 0% 0 100% 2 0% 0 100% 1 Uruguay 0% 0 0% 0 26% 18 74% 30% 3 70% 30% 3 70% 7 0% 0 100% 3 0% 0 0% 0 0% 0 0% 0 18% 2 82% 9 Venezuela 0% 0 100% 1 13% 8 87% 52 21% 4 79% 15 33% 1 67% 2 100% 1 0% 0 0% 0 0% 0 30% 3 70% 7 0% 0 0% 0 Vietnam 0% 0 0% 0 37% 7 63% 12 0% 0 100% 1 25% 1 75% 3 0% 0 0% 0 0% 0 100% 1 0% 0 0% 0 0% 0 100% 2 Zambia 0% 0 0% 0 100% 9 0% 0 0% 0 0% 0 0% 0 0% 0 100% 2 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Zimbabwe 0% 0 0% 0 31% 11 69% 25 19% 4 81% 17 100% 2 0% 0 43% 3 57% 4 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 GMMP2020 132 Who Makes the News? (p^ 22. News websites. Subjects & sources described as victims, by sex APPENDIX 5-22 Female % N VICTIM % Male N Female % N NOTAVICTIM % Male N Antigua and Barbuda 0% 0 0% 0 0% 0 0% 0 Argentina 17% 2 83% 10 24% 63 76% 205 Australia 41% 15 59% 22 34% 152 66% 298 Austria 44% 4 56% 5 27% 60 73% 166 Bangladesh 50% 4 50% 4 9% 6 91% 59 Belgium - French and Flemish 40% 2 60% 3 26% 19 74% 54 Benin 0% 0 0% 0 0% 0 0% 0 Bolivia 67% 4 33% 2 31% 47 69% 107 Bosnia and Herzegovina 50% 4 50% 4 31% 42 69% 93 Botswana 0% 0 0% 0 0% 0 0% 0 Brazil 73% 8 27% 3 25% 61 75% 182 Bulgaria 0% 0 100% 3 27% 4 73% 11 Burkina Faso 0% 0 0% 0 26% 16 74% 46 Cambodia 0% 0 0% 0 0% 0 0% 0 Cameroon 13% 1 88% 7 15% 6 85% 34 Canada 63% 5 38% 3 38% 108 62% 177 Cayman Islands 0% 0 0% 0 0% 0 0% 0 Central African Republic 0% 0 0% 0 0% 0 0% 0 Chad 0% 0 0% o 0% 0 100% 3 Chile 67% 6 33% 3 46% 56 54% 66 People's Republic of China 42% 8 58% 11 22% 76 78% 266 Colombia 0% 0 100% 3 19% 21 81% 87 Congo 0% 0 0% 0 100% 1 0% 0 Congo (Democratic Republic of the) 0% 0 0% 0 0% 0 100% 8 Costa Rica 67% 22 33% 11 34% 61 66% 121 Cuba 0% 0 0% 0 35% 48 65% 91 Cyprus 75% 3 25% 1 10% 2 90% 19 Denmark 0% 0 100% 2 31% 38 69% 86 Dominica 0% 0 0% 0 33% 1 67% 2 Dominican Republic 0% 0 100% 4 27% 39 73% 105 Ecuador 27% 4 73% 11 24% 53 76% 171 Egypt 33% 5 67% 10 13% 22 87% 153 El Salvador 83% 5 17% 1 15% 8 85% 45 Estonia 0% 0 0% 0 0% 0 0% 0 Eswatini 0% 0 0% 0 0% 0 0% 0 Ethiopia 0% 0 0% 0 25% 3 75% 9 Fiji 0% 0 0% 0 23% 7 77% 24 Finland 14% 1 86% 6 36% 90 64% 160 France 60% 18 40% 12 32% 90 68% 195 Gabon 0% 0 0% 0 0% 0 0% 0 Gambia 0% 0 0% 0 100% 1 0% 0 Georgia 0% 0 0% 0 0% 0 0% 0 Ghana 8% 1 92% 11 26% 33 74% 92 133 APPENDIX 5-22 Female % N VICTIM % Male N Female % N NOTAVICTIM % Male N Greenland 0% 0 0% 0 44% 19 56% 24 Grenada 0% 0 0% 0 0% 0 0% 0 Guatemala 17% 1 83% 5 26% 31 74% 88 Guinea 0% 0 0% 0 100% 1 0% 0 Guyana 43% 3 57% 4 38% 5 62% 8 Haiti 0% 0 100% 1 28% 20 72% 51 Hong Kong SAR PRC 0% 0 0% 0 0% 0 0% 0 Iceland 100% 1 0% 0 42% 28 58% 39 India 59% 16 41% 11 20% 62 80% 247 Indonesia 71% 10 29% 4 42% 16 58% 22 Irag 0% 0 0% 0 0% 0 0% 0 Ireland 0% 0 100% 1 19% 20 81% 83 Israel 60% 12 40% 8 21% 37 79% 136 Italy 52% 25 48% 23 25% 72 75% 211 Jamaica 33% 2 67% 4 29% 16 71% 40 Japan 83% 5 17% 1 27% 6 73% 16 Jordan 20% 5 80% 20 15% 23 85% 135 Kenya 50% 2 50% 2 19% 11 81% 46 Kyrgyzstan 50% 2 50% 2 28% 27 72% 71 Lebanon 86% 6 14% 1 21% 23 79% 88 Luxembourg 69% 9 31% 4 28% 37 72% 93 Macao 0% 0 0% 0 0% 0 0% 0 Malawi 0% 0 0% 0 40% 10 60% 15 Malaysia 100% 2 0% 0 16% 40 84% 208 Mali 20% 1 80% 4 9% 3 91% 32 Malta 46% 16 54% 19 24% 59 76% 189 Mexico 58% 11 42% 8 29% 60 71% 149 Moldova 56% 10 44% 8 34% 29 66% 56 Mongolia 67% 4 33% 2 24% 28 76% 91 Morocco 40% 2 60% 3 18% 21 82% 98 Myanmar 0% 0 0% 0 0% 0 0% 0 Namibia 0% 0 0% 0 0% 0 0% 0 Nepal 31% 11 69% 25 23% 18 77% 59 Netherlands 0% 0 0% 0 22% 20 78% 71 New Zealand 54% 7 46% 6 35% 36 65% 67 Nicaragua 0% 0 0% 0 13% 2 87% 13 Niger 0% 0 0% 0 0% 0 0% 0 Nigeria 44% 7 56% 9 21% 36 79% 136 Norway 63% 5 38% 3 23% 29 77% 96 Pakistan 32% 6 68% 13 14% 15 86% 93 Palestine 17% 4 83% 20 23% 14 77% 46 Papua New Guinea 0% 0 0% 0 0% 0 0% 0 Paraguay 0% 0 0% 0 0% 0 0% 0 Peru 0% 0 0% 0 0% 0 0% 0 Poland 80% 4 20% 1 31% 39 69% 85 Portugal 63% 5 38% 3 41% 38 59% 54 134 APPENDIX 5-22 Female VICTIM Male Female NOTAVICTIM Male % N % N % N % N Puerto Rico 77% 10 23% 3 42% 52 58% 73 Romania 43% 6 57% 8 27% 56 73% 150 Russian Federation 0% 0 0% 0 0% 0 0% 0 Senegal 0% 0 0% 0 100% 1 0% 0 Serbia 6% 2 94% 34 25% 27 75% 79 Seychelles 0% 0 0% 0 20% 2 80% 8 Sierra Leone 0% 0 0% 0 0% 0 0% 0 South Africa 0% 0 0% 0 31% 10 69% 22 South Sudan 0% 0 0% 0 0% 0 0% 0 Spain 50% 4 50% 4 24% 57 76% 179 Suriname 0% 0 100% 1 5% 2 95% 41 Sweden 60% 9 40% 6 33% 72 67% 145 Switzerland 55% 16 45% 13 27% 171 73% 460 Taiwan Province of China 40% 8 60% 12 40% 81 60% 121 Tanzania 100% 1 0% 0 30% 3 70% 7 Togo 0% 0 0% 0 31% 5 69% 11 Trinidad and Tobago 0% 0 0% 0 0% 0 0% 0 Tunisia 60% 3 40% 2 21% 26 79% 99 Turkey 44% 17 56% 22 20% 54 80% 221 Uganda 0% 0 0% 0 0% 0 0% 0 United Kingdom 37% 18 63% 31 27% 125 73% 331 United States of America 67% 2 33% 1 32% 21 68% 44 Uruguay 100% 2 0% 0 24% 24 76% 77 Venezuela 0% 0 0% 0 15% 11 85% 61 Vietnam 50% 2 50% 2 30% 8 70% 19 Zambia 0% 0 0% 0 0% 0 0% 0 Zimbabwe 0% 0 0% 0 0% 0 0% 0 GMMP2020 135 VVI 23. News websites. Subjects and sources who are quoted directly, by sex APPENDIX 5-23 YES NO Female Male Female Male % N % N % N % N Antigua and Barbuda 0% 0 0% 0 0% 0 0% 0 Argentina 24% 43 76% 136 20% 20 80% 79 Australia 33% 79 67% 161 34% 78 66% 149 Austria 28% 40 72% 104 26% 24 74% 68 Bangladesh 25% 3 75% 9 9% 5 91% 51 Belgium-French and Flemish 28% 11 73% 29 22% 8 78% 28 Benin 0% 0 0% 0 0% 0 0% 0 Bolivia 25% 20 75% 61 39% 31 61% 48 Bosnia and Herzegovina 27% 26 73% 70 40% 16 60% 24 Botswana 0% 0 0% 0 0% 0 0% 0 Brazil 25% 29 75% 86 27% 36 73% 97 Bulgaria 11% 1 89% 8 50% 4 50% 4 Burkina Faso 30% 11 70% 26 20% 5 80% 20 Cambodia 0% 0 0% 0 0% 0 0% 0 Cameroon 15% 6 85% 33 0% 0 100% 3 Canada 38% 78 63% 130 41% 34 59% 49 Cayman Islands 0% 0 0% 0 0% 0 0% 0 Central African Republic 0% 0 0% 0 0% 0 0% 0 Chad 0% 0 100% 1 0% 0 100% 2 Chile 49% 41 51% 42 39% 17 61% 27 People's Republic of China 20% 38 80% 155 28% 47 72% 120 Colombia 19% 12 81% 51 18% 8 82% 37 Congo 100% 1 0% 0 0% 0 0% 0 Congo (Democratic Republic of the) 0% 0 100% 8 0% 0 0% 0 Costa Rica 38% 51 62% 84 39% 27 61% 42 Cuba 38% 19 62% 31 33% 29 67% 60 Cyprus 0% 0 100% 9 31% 5 69% 11 Denmark 31% 39 69% 85 0% 0 100% 3 Dominica 33% 1 67% 2 0% 0 0% 0 Dominican Republic 21% 8 79% 31 28% 31 72% 78 Ecuador 26% 37 74% 105 20% 20 80% 78 Egypt 8% 2 92% 24 15% 25 85% 137 El Salvador 21% 6 79% 23 23% 7 77% 23 Estonia 0% 0 0% 0 0% 0 0% 0 Eswatini 0% 0 0% 0 0% 0 0% 0 Ethiopia 25% 3 75% 9 0% 0 0% 0 Fiji 9% 1 91% 10 30% 6 70% 14 Finland 37% 55 63% 95 34% 36 66% 71 France 37% 78 63% 132 28% 28 72% 72 Gabon 0% 0 0% 0 0% 0 0% 0 Gambia 0% 0 0% 0 100% 1 0% 0 Georgia 0% 0 0% 0 0% 0 0% 0 Ghana 28% 32 72% 81 12% 2 88% 15 GMMP 2020 136 Who Makes the News? APPENDIX 5-23 YES NO Female Male Female Male % N % N % N % N Greenland 50% 17 50% 17 29% 2 71% 5 Grenada 0% 0 0% 0 0% 0 0% 0 Guatemala 25% 27 75% 82 40% 6 60% 9 Guinea 0% 0 0% 0 100% 1 0% 0 Guyana 0% 0 100% 5 45% 5 55% 6 Haiti 32% 6 68% 13 30% 14 70% 33 Hong Kong SAR PRC 42% 30 58% 41 42% 30 58% 41 Iceland 43% 20 57% 26 40% 8 60% 12 India 20% 21 80% 86 22% 48 78% 170 Indonesia 8% 1 92% 12 60% 15 40% 10 Irag 0% 0 0% 0 0% 0 0% 0 Ireland 22% 17 78% 61 12% 3 88% 22 Israel 25% 24 75% 73 24% 22 76% 69 Italy 28% 37 72% 96 29% 55 71% 135 Jamaica 31% 14 69% 31 21% 3 79% 11 Japan 40% 10 60% 15 33% 1 67% 2 Jordan 4% 1 96% 22 16% 27 84% 137 Kenya 12% 3 88% 22 26% 9 74% 26 Kyrgyzstan 24% 11 76% 35 29% 15 71% 37 Lebanon 23% 20 77% 67 23% 8 77% 27 Luxembourg 34% 22 66% 43 29% 22 71% 54 Macao 36% 4 64% 7 34% 14 66% 27 Malawi 53% 8 47% 7 20% 2 80% 8 Malaysia 14% 13 86% 81 19% 30 81% 127 Mali 10% 3 90% 28 0% 0 100% 6 Malta 23% 20 77% 67 27% 51 73% 137 Mexico 28% 43 72% 112 40% 28 60% 42 Moldova 49% 26 51% 27 26% 12 74% 35 Mongolia 24% 10 76% 31 26% 20 74% 58 Morocco 19% 15 81% 66 18% 7 82% 32 Myanmar 28% 13 72% 34 0% 0 100% 2 Namibia 0% 0 0% 0 0% 0 0% 0 Nepal 15% 9 85% 50 30% 16 70% 38 Netherlands 20% 8 80% 32 24% 12 76% 39 New Zealand 37% 32 63% 54 37% 11 63% 19 Nicaragua 0% 0 0% 0 13% 2 87% 13 Niger 40% 4 60% 6 0% 0 0% 0 Nigeria 14% 9 86% 54 25% 27 75% 81 Norway 27% 26 73% 70 20% 7 80% 28 Pakistan 20% 9 80% 36 13% 10 87% 67 Palestine 19% 4 81% 17 21% 13 79% 49 Papua New Guinea 9% 1 91% 10 40% 2 60% 3 Paraguay 33% 17 67% 34 8% 1 92% 11 Peru 22% 11 78% 40 35% 7 65% 13 Poland 31% 29 69% 64 40% 14 60% 21 Portugal 45% 25 55% 30 40% 17 60% 25 GMMP2020 137 Who Makes the News? APPENDIX 5-23 YES NO Female Male Female Male % N % N % N % N Puerto Rico 48% 31 52% 34 43% 31 57% 41 Romania 30% 42 70% 98 24% 18 76% 57 Russian Federation 34% 36 66% 71 27% 39 73% 104 Senegal 11% 5 89% 42 17% 4 83% 20 Serbia 29% 20 71% 50 13% 8 88% 56 Seychelles 17% 1 83% 5 33% 1 67% 2 Sierra Leone 0% 0 0% 0 0% 0 0% 0 South Africa 33% 10 67% 20 0% 0 100% 2 South Sudan 0% 0 0% 0 0% 0 0% 0 Spain 24% 45 76% 140 27% 12 73% 33 Suriname 0% 0 100% 10 6% 2 94% 30 Sweden 33% 49 67% 100 36% 27 64% 48 Switzerland 28% 77 72% 194 27% 105 73% 279 Taiwan Province of China 46% 41 54% 49 37% 46 63% 78 Tanzania 30% 3 70% 7 0% 0 0% 0 Togo 38% 3 63% 5 25% 2 75% 6 Trinidad and Tobago 0% 0 0% 0 0% 0 0% 0 Tunisia 8% 2 92% 23 27% 28 73% 75 Turkey 16% 23 84% 117 25% 37 75% 110 Uganda 0% 0 0% 0 0% 0 0% 0 United Kingdom 30% 111 70% 259 24% 31 76% 96 United States of America 33% 15 67% 31 32% 6 68% 13 Uruguay 33% 17 67% 34 17% 9 83% 43 Venezuela 16% 12 84% 62 21% 4 79% 15 Vietnam 17% 1 83% 5 33% 7 67% 14 Zambia 100% 2 0% 0 100% 9 0% 0 Zimbabwe 20% 7 80% 28 42% 13 58% 18 GMMP2020 138 Who Makes the News? 24. News websites and news media tweets. Subjects & sources appearing in images and video pLug-ins, by sex INTERNET Female % N Yes % Male N Female % N No % Male N Female % N Do not know % Male N Antigua and Barbuda 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Argentina 17% 11 83% 52 25% 53 75% 163 0% 0 100% 1 Australia 32% 51 68% no 35% 106 65% 200 0% 0 0% 0 Austria 29% 20 71% 50 26% 42 74% 122 100% 1 0% 0 Bangladesh 0% 0 100% 8 13% 8 87% 52 0% 0 0% 0 Belgium 29% 12 71% 29 22% 8 78% 28 0% 0 0% 0 Benin 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Bolivia 30% 17 70% 39 33% 33 67% 68 33% 1 67% 2 Bosnia and Herzegovina 30% 20 70% 46 32% 23 68% 48 0% 0 100% 1 Botswana 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Brazil 33% 18 67% 37 24% 47 76% 148 100% 3 0% 0 Bulgaria 29% 2 71% 5 30% 3 70% 7 0% 0 0% 0 Burkina Faso 27% 10 73% 27 13% 1 88% 7 29% 5 71% 12 Cambodia 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Cameroon 8% 2 92% 22 22% 4 78% 14 0% 0 0% 0 Canada 50% 4 50% 4 31% 16 69% 35 0% 0 0% 0 Cayman Islands 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Central African Republic 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Chad 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Chile 57% 12 43% 9 43% 46 57% 60 0% 0 0% 0 People's Republic of China 30% 33 70% 77 21% 42 79% 155 21% 4 79% 15 Colombia 4% 1 96% 22 23% 20 77% 67 0% 0 0% 0 Congo 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 Congo (Democratic Republic of the) 0% 0 100% 3 0% 0 100% 5 0% 0 0% 0 Costa Rica 45% 22 55% 27 36% 54 64% 94 33% 2 67% 4 Cuba 28% 8 72% 21 38% 36 62% 59 25% 4 75% 12 Cyprus 0% 0 100% 2 22% 5 78% 18 0% 0 0% 0 Denmark 20% 3 80% 12 32% 36 68% 76 0% 0 0% 0 Dominica 100% 1 0% 0 0% 0 100% 2 0% 0 0% 0 Dominican Republic 26% 11 74% 32 26% 26 74% 75 50% 2 50% 2 Ecuador 31% 16 69% 35 22% 41 78% 147 0% 0 100% 1 Egypt 9% 4 91% 40 16% 23 84% 122 0% 0 0% 0 El Salvador 20% 4 80% 16 24% 8 76% 25 17% 1 83% 5 Estonia 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Eswatini 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Ethiopia 20% 1 80% 4 33% 2 67% 4 0% 0 0% 0 Fiji 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 139 TWITTER Yes No Do not know Female Male Female Male Female Male % N % N % N % N % N % N 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 21% 20 79% 76 28% 44 72% 114 14% 1 86% 6 22% 9 78% 32 25% 5 75% 15 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 25% 7 75% 21 33% 2 67% 4 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 27% 12 73% 32 33% 5 67% 10 0% 0 0% 0 0% 0 100% 11 50% 2 50% 2 0% 0 0% 0 30% 3 70% 7 50% 2 50% 2 0% 0 0% 0 32% 12 68% 26 27% 3 73% 8 0% 0 0% 0 57% 4 43% 3 40% 2 60% 3 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 100% 6 0% 0 100% 16 0% 0 100% 3 36% 16 64% 29 43% 3 57% 4 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 33% 1 67% 2 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 32% 7 68% 15 33% 1 67% 2 0% 0 0% 0 12% 4 88% 30 27% 13 73% 35 0% 0 100% 5 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 38% 10 62% 16 27% 19 73% 51 75% 3 25% 1 20% 1 80% 4 17% 1 83% 5 0% 0 0% 0 0% 0 100% 10 23% 3 77% 10 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 44% 4 56% 5 50% 3 50% 3 0% 0 100% 1 30% 6 70% 14 20% 17 80% 66 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 21% 4 79% 15 25% 3 75% 9 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Who Makes the News? (p^ Yes No Do not know Yes No Do not know Female Male Female Male Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N % N % N % N % N Finland 37% 42 63% 71 34% 49 66% 94 0% 0 0% 0 23% 9 78% 31 28% 7 72% 18 0% 0 0% 0 France 30% 14 70% 32 34% 89 66% 170 0% 0 0% 0 39% 30 61% 46 40% 21 60% 31 0% 0 0% 0 Gabon 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Gambia 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 50% 1 50% 1 100% 1 0% 0 0% 0 0% 0 Georgia 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Ghana 20% 15 80% 59 32% 19 68% 41 0% 0 0% 0 17% 20 83% 96 4% 2 96% 51 0% 0 0% 0 Greenland 56% 9 44% 7 37% 10 63% 17 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Grenada 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Guatemala 24% 10 76% 31 26% 20 74% 56 43% 3 57% 4 15% 4 85% 22 40% 6 60% 9 25% 1 75% 3 Guinea 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Guyana 0% 0 100% 10 83% 5 17% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Haiti 31% 10 69% 22 26% 10 74% 29 0% 0 0% 0 11% 1 89% 8 20% 6 80% 24 0% 0 0% 0 Hong Kong SAR PRC 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 49% 17 51% 18 62% 8 38% 5 50% 1 50% 1 Iceland 41% 17 59% 24 46% 12 54% 14 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 India 25% 17 75% 50 20% 48 80% 188 18% 4 82% 18 25% 18 75% 55 39% 12 61% 19 0% 0 0% 0 Indonesia 47% 7 53% 8 36% 8 64% 14 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Irag 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Ireland 18% 3 82% 14 20% 17 80% 69 0% 0 0% 0 45% 9 55% 11 0% 0 100% 26 0% 0 0% 0 Israel 36% 20 64% 36 20% 26 80% 105 0% 0 100% 1 11% 2 89% 16 35% 8 65% 15 0% 0 0% 0 Italy 38% 32 62% 52 27% 64 73% 177 0% 0 100% 2 27% 24 73% 65 19% 7 81% 30 0% 0 0% 0 Jamaica 31% 11 69% 25 28% 7 72% 18 0% 0 0% 0 38% 10 62% 16 29% 4 71% 10 0% 0 0% 0 Japan 53% 8 47% 7 23% 3 77% 10 0% 0 0% 0 0% 0 100% 36 24% 9 76% 29 0% 0 0% 0 Jordan 10% 4 90% 36 17% 25 83% 122 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Kenya 22% 2 78% 7 20% 10 80% 41 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Kyrgyzstan 18% 6 82% 27 32% 22 68% 47 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Lebanon 24% 14 76% 45 24% 15 76% 47 0% 0 100% 2 22% 12 78% 42 14% 5 86% 31 0% 0 0% 0 Luxembourg 19% 7 81% 29 35% 37 65% 68 0% 0 0% 0 15% 2 85% 11 50% 3 50% 3 0% 0 0% 0 Macao 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Malawi 50% 6 50% 6 31% 4 69% 9 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Malaysia 33% 10 67% 20 24% 4 76% 13 0% 0 0% 0 25% 2 75% 6 11% 1 89% 8 0% 0 0% 0 Mali 0% 0 100% 12 12% 3 88% 22 0% 0 0% 0 0% 0 100% 4 0% 0 100% 2 0% 0 0% 0 Malta 22% 18 78% 64 28% 53 72% 137 0% 0 100% 1 14% 5 86% 31 21% 6 79% 22 100% 1 0% 0 Mexico 27% 23 73% 61 32% 45 68% 94 100% 1 0% 0 36% 35 64% 62 38% 25 62% 40 57% 4 43% 3 Moldova 44% 21 56% 27 32% 17 68% 36 0% 0 0% 0 22% 12 78% 42 17% 9 83% 45 0% 0 0% 0 Mongolia 25% 14 75% 43 25% 15 75% 45 0% 0 100% 2 14% 3 86% 19 0% 0 100% 2 0% 0 0% 0 Morocco 28% 8 72% 21 15% 14 85% 77 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Myanmar 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Namibia 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Nepal 35% 6 65% 11 21% 20 79% 75 25% 1 75% 3 23% 3 77% 10 38% 5 62% 8 0% 0 0% 0 Netherlands 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 29% 9 71% 22 10% 1 90% 9 0% 0 0% 0 140 Yes No Do not know Yes No Do not know Female Male Female Male Female Male Female Male Female Male Female Male % N % N % N % N % N % N % N % N % N % N % N % N New Zealand 47% 14 53% 16 34% 29 66% 57 0% 0 0% 0 42% 8 58% 11 40% 4 60% 6 0% 0 0% 0 Nicaragua 33% 1 67% 2 8% 1 92% 11 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Niger 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Nigeria 24% 19 76% 59 20% 20 80% 80 0% 0 0% 0 21% 7 79% 27 60% 3 40% 2 100% 2 0% 0 Norway 32% 22 68% 47 18% 11 82% 51 0% 0 0% 0 16% 6 84% 32 11% 2 89% 17 0% 0 100% 3 Pakistan 29% 12 71% 30 7% 6 93% 78 100% 1 0% 0 35% 9 65% 17 50% 8 50% 8 0% 0 0% 0 Palestine 22% 10 78% 35 18% 7 82% 31 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Papua New Guinea 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Paraguay 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 13% 3 87% 20 12% 3 88% 22 0% 0 0% 0 Peru 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Poland 39% 21 61% 33 29% 22 71% 53 0% 0 0% 0 27% 8 73% 22 32% 8 68% 17 0% 0 0% 0 Portugal 53% 19 47% 17 38% 25 62% 40 0% 0 0% 0 25% 4 75% 12 24% 4 76% 13 0% 0 100% 5 Puerto Rico 59% 20 41% 14 40% 40 60% 61 0% 0 0% 0 41% 16 59% 23 25% 1 75% 3 0% 0 0% 0 Romania 31% 34 69% 74 25% 27 75% 82 0% 0 0% 0 100% 9 0% 0 100% 1 0% 0 0% 0 0% 0 Russian Federation 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Senegal 33% 4 67% 8 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Serbia 24% 17 76% 53 17% 11 83% 53 0% 0 0% 0 44% 12 56% 15 14% 2 86% 12 100% 2 0% 0 Seychelles 25% 1 75% 3 17% 1 83% 5 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Sierra Leone 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 South Africa 56% 5 44% 4 22% 5 78% 18 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 South Sudan 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Spain 35% 21 65% 39 21% 36 79% 134 0% 0 0% 0 36% 26 64% 47 16% 4 84% 21 33% 2 67% 4 Suriname 0% 0 100% 19 9% 2 91% 21 0% 0 0% 0 14% 1 86% 6 18% 3 82% 14 0% 0 100% 1 Sweden 40% 34 60% 50 29% 40 71% 96 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Switzerland 30% 106 70% 251 25% 71 75% 215 43% 3 57% 4 29% 16 71% 40 14% 7 86% 44 100% 2 0% 0 Taiwan Province of China 42% 45 58% 63 40% 42 60% 64 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Tanzania 30% 3 70% 7 0% 0 0% 0 0% 0 0% 0 0% 0 100% 20 0% 0 0% 0 0% 0 0% 0 Togo 33% 3 67% 6 33% 2 67% 4 0% 0 100% 1 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Trinidad and Tobago 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 25% 1 75% 3 0% 0 100% 2 0% 0 100% 1 Tunisia 22% 7 78% 25 23% 23 77% 77 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Turkey 24% 40 76% 125 17% 19 83% 94 11% 1 89% 8 25% 36 75% 109 15% 9 85% 51 0% 0 100% 7 Uganda 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 United Kingdom 32% 47 68% 99 28% 95 72% 249 0% 0 100% 7 37% 38 63% 64 16% 7 84% 37 0% 0 0% 0 United States of America 41% 11 59% 16 21% 3 79% 11 0% 0 100% 1 50% 2 50% 2 0% 0 100% 3 0% 0 0% 0 Uruguay 22% 8 78% 29 27% 18 73% 48 0% 0 0% 0 8% 4 92% 46 29% 18 71% 44 0% 0 0% 0 Venezuela 20% 3 80% 12 12% 6 88% 45 33% 2 67% 4 28% 28 72% 72 29% 10 71% 25 33% 1 67% 2 Vietnam 67% 4 33% 2 19% 4 81% 17 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Zambia 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 Zimbabwe 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 0% 0 141 25. News websites and news media tweets.Reporters in major topic areas, by sex INTERNET I Crime and VI 26. News websites and news media tweets. Responses to "This story deary challenges gender stereotypes" APPENDIX 5-26 INTERNET TWITTER Agree Disagree Agree Disagree % N % N Antigua and Barbuda 29% 6 71% 15 Argentina 2% 5 98% 215 Australia 2% 8 98% 371 Austria 0% 0 100% 64 Bangladesh 0% 0 100% 229 Belgium 1% 1 100% 199 Benin 3% 2 97% 65 Bolivia 7% 52 93% 407 Bosnia and Herzegovina 1% 2 99% 184 Botswana 14% 9 86% 55 Brazil 2% 4 98% 233 Bulgaria 5% 3 95% 61 Burkina Faso 1% 2 99% 162 Cambodia 17% 7 83% 34 Cameroon 8% 11 92% 121 Canada 4% 15 96% 302 Cayman Islands 0% 0 100% 40 Central African Republic 0% 0 100% 22 Chad 5% 2 95% 36 Chile 4% 11 96% 264 People's Republic of China 0% 0 100% 329 Colombia 2% 3 98% 137 Congo 0% 0 100% 8 Congo (Democratic Republic of the) 2% 3 98% 158 Costa Rica 6% 15 94% 221 Cuba 0% 0 100% 106 Cyprus 4% 7 97% 193 Denmark 4% 7 96% 157 Dominica 0% 0 100% 20 Dominican Republic 0% 0 100% 85 Ecuador 3% 5 97% 193 Egypt 3% 4 97% 150 El Salvador 7% 7 93% 92 Estonia 0% 0 100% 115 Eswatini 3% 5 97% 139 Ethiopia 3% 4 97% 112 APPENDIX 5-26 INTERNET TWITTER Agree Disagree Agree Disagree % N % N Fiji 1% 1 99% 66 Finland 2% 3 98% 172 France 3% 14 97% 443 Gabon 21% 3 79% 11 Gambia 21% 7 79% 27 Georgia 0% 3 100% 681 Ghana 1% 4 99% 533 Greenland 3% 2 97% 75 Grenada 0% 0 100% 44 Guatemala 3% 6 97% 215 Guinea 0% 0 100% 18 Guyana 0% 0 100% 29 Haiti 3% 3 97% 104 Hong Kong SAR PRC 0% 0 100% 194 ceiand 3% 2 98% 78 India 7% 36 93% 450 Indonesia 10% 7 90% 60 Iraq 11% 1 89% 8 Ireland 3% 4 97% 154 Israel 1% 1 99% 103 Italy 5% 10 95% 210 Jamaica 2% 2 98% 124 Japan 2% 2 98% 115 Jordan 3% 8 97% 278 Kenya 7% 7 93% 98 Kyrgyzstan 1% 2 99% 156 Lebanon 0% 0 100% 80 Luxembourg 1% 1 99% 95 Macao 0% 0 100% 137 Malawi 3% 4 97% 142 Malaysia 1% 2 99% 279 Mali 5% 4 97% 125 Malta 4% 8 96% 177 Mexico 10% 57 90% 528 Moldova 1% 2 99% 186 Mongolia 0% 0 100% 133 GMMP 2020 145 Who Makes the News? APPENDIX 5-26 INTERNET TWITTER Agree % Disagree N Agree % Disagree N Morocco 2% 3 98% 141 Myanmar 6% 6 94% 90 Namibia 12% 5 88% 36 Nepal 5% 18 95% 323 Netherlands 3% 4 97% 117 New Zealand 1% 1 99% 113 Nicaragua 12% 11 88% 84 Niger 11% 4 89% 52 Nigeria 1% 2 99% 177 Norway 7% 13 93% 167 Pakistan 2% 6 98% 266 Palestine 0% 0 100% 116 Papua New Guinea 19% 14 81% 59 Paraguay 1% 1 99% 167 Peru 2% 5 98% 321 Poland 1% 3 99% 317 Portugal 2% 4 98% 181 Puerto Rico 8% 8 92% 97 Romania 2% 5 98% 266 Russian Federation 1% 1 99% 81 Senegal 2% 1 98% 41 Serbia 0% 0 100% 196 Seychelles 0% 0 100% 29 Sierra Leone 43% 3 57% 4 South Africa 2% 2 98% 131 South Sudan 0% 0 100% 48 Spain 5% 19 95% 377 Suriname 1% 1 99% 77 Sweden 5% 10 95% 204 Switzerland 10% 31 90% 295 Taiwan Province of China 0% 0 100% 282 Tanzania 8% 16 92% 174 Togo 9% 8 91% 81 Trinidad and Tobago 7% 4 93% 55 Tunisia 3% 7 97% 245 Turkey 2% 13 98% 668 Uganda 1% 1 99% 113 United Kingdom 3% 13 97% 467 United States of America 17% 32 83% 156 Uruguay 1% 5 99% 357 Venezuela 6% 17 94% 245 APPENDIX 5-26 INTERNET TWITTER Agree Disagree Agree Disagree % N % N Vietnam 17% 8 83% 38 Zambia 24% 6 76% 19 Zimbabwe 1% 1 99% 97 GMMP 2020 146 Who Makes the News? ANNEX 6 List of coordinators AFRICA Regional Coordinators Eastern Africa African Woman and Child Features Service Arthur Okwemba, Kenya West & Centra [Africa Reseau Inter -Africain pour les Femmes, Medias, Genre et Developpement Amie Joof/ Medoune Seek, Senegal Southern Africa Gender Links Tarisai Nyamweda, South Africa National Coordinators Benin ONG FAMEDEV Bismarck Sossa Botswana WIN WAN-IFRA Phiri Lubwika / Bot Botswana Women in News Network Boitshepo Balozwi Burkina Faso ONG FAMEDEV Ali Taonsa Cameroon Women's Peace Initiative Nathalie Foko Central African Republic ONG Comite pour le Developpement Integre des communautes de base Limbingo Ngakeu Chad ONG FAMEDEV Constant Mbailassem Congo Syndicat des Journa listes du Congo Edouard Adzotsa Congo (Democratic Republic of the) Si Jeunesse Savait / Union Congolaise des Femmes des Medias Francoise Mukuku/Anna Mayimona Ngemba Eswatini University of Eswatini Maxwell Mthembu Ethiopia Ethiopian Media Women Association Tekabech Assefa Gabon ONG FAMEDEV Georgina Mefane Lea Eyeng Gambia Gambia Press Union Bai EmilTouray Ghana Ghana Broadcasting Corporation Charity Binka Guinea ONG FAMEDEV Kadiatou Thierno Diallou Kenya African Woman and Child Features Service Arthur Okwemba Malawi Youth and Children Shield Bright Kampaundi Mali ONG FAMEDEV Saran Keita Namibia Namibia University of Science and Technology Emily Brown Niger ONG FAMEDEV Yvette Dovi Nigeria Media and Gender Enlightenment Initiative Nkem Theresa Fab-Ukozor/Alex Onyebuchi Senegal ONG FAMEDEV Amie Joof Cole / Medoune Seek Seychelles Gender and Media Plus Association of Seychelles Sharon Thelemaque Sierra Leone Initiatives for Media Development Yeama Sarah Thompson South Africa Gender Links Tarisai Nyamweda South Sudan Association of Media Women in Southern Sudan Veronica Lucy Gordon / Lily Nelson Tanzania Gender and Media in Southern Africa - Tanzania Network Gladness Hemedi Munuo Togo ONG FAMEDEV/UJIT Yaovi Tchalim Honore Blao/Ali Tagba Khadi Uganda Uganda Media Women's Association Margaret Sentamu-Masagazi Zambia Media Institute of Southern Africa Henry Kabwe Zimbabwe Gender and Media Connect Abigail Gamanya GMMP2020 147 Who Makes the News? ASIA Regional Coordinator University of Dakha Gitiara Nasreen, Bangladesh National Coordinators Bangladesh University of Dhaka Gitiara Nasreen Bangladesh Amrai Pari Jot (WE CAN BANGLADESH) Jamilur Rahman Cambodia Cambodian Centre for Independent Media Dani Caspe/Kalyan Sann China Mainland China (PRC) Women Network in China Feng Yuan Macao SAR (PRC) Associacao de Imprensa em Portugues e Ingles de Macau Maria Salome Fernandes Hong Kong SAR (PRC) Chinese University of Hong Kong Sara Liao Taiwan Province of China National Chengchi University Leticia Nien-Hsuan Fang India Network of Women in Media, India Ammu Joseph / Padmaja Shaw Indonesia Alliance of Independent Journalists Yekti Hesthi Murthi Japan Forum for Citizens TV & Media Kyoko Takahashi Kyrgyzstan Forum of Women's NGOs of Kyrgyzstan Chinara Kartanbaeva Malaysia Universiti Sains Malaysia Wang Lay Kim Mongolia Press Institute of Mongolia Unurjargal Lkhanaa / Oyuntsetseg Ravdan Myanmar Myanmar Women's Journalist Society Soe San Htike / Tin Zar Aung Nepal Asmita Women's Publishing House, Media & Resource Organization Sarita Shrestha Pakistan Uks-Research, Resource and Publication Centre on Women and Media Tasneem Ahmar Vietnam Research centre for Gender, Family and Environment in Development PhamThi Minh Hang CARIBBEAN Regional Coordinators English speaking Caribbean: WMW-Jamaica Hilary Nicholson.Jamaica French & Spanish speaking Caribbean: Red de Investigacion y Colaboracion en Comuni-cacion de Centra America y el Caribe Maximiliano Duehas-Guzmän, Puerto Rico National Coordinators Antigua and Barbuda Women Against Rape Inc. Alexandria Wong Cayman Islands Gender Affairs Unit, Ministry of Community Affairs Karlene Bramwell Cuba Christian Institute of Gender Studies Sara Mas Dominica National Women's Council Vanya David Dominican Republic Espacio de Comunicacion Insular Solange de la Cruz Matos/Jose Luis Soto Rodriguez Grenada Grenada National Organisation of Women Bernadette Bartholomew Guyana Artists in Direct Support Guyana Desiree Edgehill Haiti Rezo Fanm Radyo Kominote Ayisyen Marie Guyrleine Justin Jamaica WMW-Jamaica Hilary Nicholson / Lisandria Thompson Puerto Rico Universidad de Puerto Rico Lourdes Lugo-Ortiz GMMP2020 148 Who Makes the News? Suriname Caribbean Association for Feminist Research and Sandra Qenem Action Trinidad & Tobago Network of NGOS of Trinidad and Tobago for the Nicole Hendrickson Advancement of Women EUROPE Regional Coordinator National Coordinators Austria Belgium Belgium Flemish Belgium French Bosnia & Herzegovina Bulgaria Cyprus Denmark Estonia Finland France University of Newcastle Karen Ross, United Kingdom Universität Salzburgare University of Ghent Universitě Libre de Bruxelles Novi Put People & Borders Foundation Mediterranean Institute of Gender Studies Roskilde University Eesti People to People NGO University of Helsinki Universitě de Toulouse II Lisa Schulze Sara de Vuyst / Sofie Van Bauwel Florence Le Cam Abida Pehlic I liana Stoicheva Maria Angeli / Susana Pavlou Hanne Jerndrup Ruta Pels Jonita Siivonen Marlene Coulomb-Gully France Georgia Greenland Iceland Ireland Italy Luxembourg Malta Moldova Mines ParisTech Journalists Association Gender Media Caucasus University of Greenland University of Iceland Dublin City University Osservatorio di Pavia / University of Padova CID, Femmes et genre University of Malta Gender Media Academy Cecile Meadel Galina Petriashvili Signe Ravn-Hejgaard/Naimah Hussain Valgeräur Jóhannsdóttir Dawn Wheatley Monia Azzalini / Claudia Padovani Isabelle Schmoetten Brenda Murphy Victoria Puiu Netherlands Norway Poland Portugal Romania Russian Federation Women Inc Oslo and Akershus University College University of Lodz Institute de EstudosJornalisticos University of Bucharest Institute of Socio-Economic Studies of Population - Branch of the Federal Center of Theoretical and Applied Sociology of the Russian Academy of Sciences Elze Ghijsen Elisabeth Eide / Kristin S. Orgeret Greta Gober Rita Basilio Simöes Daniela Roventa-Frumusani Yulia Nenakhova University of Belgrade - Faculty of Political Science Zenski informativno-dokumentarni centar Universidad de Malaga University of Gothenburg Bureau de I'egalite entre les femmes et les hommes Anadolu University Serbia Serbia Spain Sweden Switzerland Turkey Snjezana Milivojevic Violeta Andjelkovic-Kanzleiter Maria Teresa Vera Balanza Maria Edström Kathrin Egolf Nezih Orhon GMMP2020 149 Who Makes the News? United Kingdom England Northern Ireland Scotland Wales University of Newcastle Ulster University Strathclyde University Cardiff University Karen Ross (England) Bethany Waterhouse Bradley (Northern Ireland) Karen Boyle (Scotland) Cynthia Carter (Wales) LATIN AMERICA Regional Coordinator National Coordinators Argentina Bolivia Brazil Chile Colombia Costa Rica Ecuador El Salvador Guatemala Grupo de Apoyo al Movimiento de Mujeres delAzuay Sandra Lopez Astudillo, Ecuador Comunicacion para la Igualdad Universidad Catolica Boliviana -SECRAD University of Coimbra Consejo Nacional de Television de Chile Comunidad Teologica Evangelica de Chile Universidad del Rosario Observatorio de Genero y Medios Marcela Gabioud / Claudia Florentin / Maria Soledad Ceballos Lorna Arauz Rodríguez/José LuisAguirre Alvis Elizángela Carvalho Maria Elena Hermosilla Benjamín RodríguezAvendaňo DanghellyZuňiga Reyes Vilma Peňa-Vargas Grupo de Apoyo al Movimiento de Mujeres delAzuay Universidad Centroamericana"José Simeón Caňas" Red de Mujeres aL Aire Sandra López Astudillo/ Gabriela Avila Paredes Amparo Marroquín / Marisela Morán / Serafín Valencia Elena Patricia Galicia Nuňez Mexico Nicaragua Paraguay Peru Uruguay Venezuela Comunicación e Información de la Mujer ITESO - Universidad Jesuita de Guadalajara Radio Universidad Kuna Róga Asociación de Comunicadores Sociales Calandria Cotidiano Mujer Asociación Civil Mujeres en Linea Cirenia Celestino Ortega Magdalena Sofia Palau Cardona Nelson Rodriguez Navarrete Lizandra Rolon Lopez Marisol Castaheda / Rosario Ouijandria Francesca Casariego Luisa Kislinger MIDDLE EAST Regional Coordinator National Coordinators Egypt Iraq Israel Appropriate Communication Techniques for Development Appropriate Communication Techniques for Development Internews Sapir College Azza Kamel, Egypt Azza Kamel Raber Kaluri Einat Lachover Jordan Lebanon Arab Women's Organizations of Jordan Maharat Foundation ManalAltaleb Tony Mikhael GMMP2020 150 Who Makes the News? Morocco Palestine Tunisia Moroccan High Authority for Audiovisual Communication Women, Media and Development Center for Arab Women Training and Research Latifa Ourtassi Tayah Suheir Farraj / Maha Zghary Lobna Najar/Atidel Mejbri NORTH AMERICA National Coordinators Canada News Correspondent/Video Journalist/News Researcher Veronica Silva Cusi United States of America United Methodist Women Glory Dharmaraj / Yvette Moore PACIFIC National Coordinators Australia Queensland University of Technology Angela Romano Fiji Fiji Media Watch Group Agatha Ferei Furivai / Mereoni Raivalita / Ruberta Ferei NewZealand MasseyUniversity Susan Fountaine Papua New Guinea Divine Word University Naomi. A.G.Woyengu Papua New Guinea PNG YWCA Jethro San Juan GMMP 2020 151 Who Makes the News? ANNEX 7 Technical advisory committee Arnie Joof, Inter-African Network for Women, Media, Gender and Development - FAMEDEV, Senegal) Azza Kamel, (Appropriate Communication Techniques for Development-ACT, Egypt) Claudia Padovani (University of Padova, Italy) Gitiara Nasreen (University of Dhaka, Bangladesh) Hilary Nicholson (WMW-Jamaica) Jonita Siivonen (University of Helsinki, Finland) Karen Ross (Newcastle University, UK) Maha Al-Zghary (Women Media and Development-TAM, Palestine) Margaret Sentamu (Uganda Media Women's Association -UMWA) Maximiliano Duenas Guzman (University of Puerto Rico) Sandra Lopez (GAMMA, Ecuador) Suheir Farraj (Women Media and Development- TAM, Palestine) Tasneem Ahmar (Uks research centre, Pakistan) GMMP2020 152 Who Makes the News? ANNEX 8 Resources for Journalists BBC, 50:50: The Equality Project, the biggest collective action on increasing BBC content that there's ever been. Website:https ://www. bb c. co. uk/5050 Summary: 50:50: The Equality Project began four years ago "as a simple idea to measure and increase representation of women on one BBC news programme." It has since grown to include not just news but all content produced on all its platforms. It uses a methodology "that is rooted in data, creativity, practicality and passion to fundamentally shift representation within the media." Bureau of International Information Programs, US Department of State, Global Women's Issues: Women in the World Today, Extended Version, Chapter 10, Women and the Media Website: https://opentextbc.ca/womenintheworld/chapter/ chapter- 10-women-and-the-media/ Language: English Summary: Provides history of exclusion and stereotypes, as well as the new era of women's rights. Offers examples of media outlets with women CEOs who revamped their publications for a digital age. Columbia Journalism Review, You're probably not quoting enough women. Let us help you, by Alexandria Neason. Website: https://www.cjr.org/analysis/women-sources.php Summary: A compilation of a public database of women, nonbinary, and people of colour from around the world who are experts on the media. It also invites people to submit additional names and contact information for sources. Committee to Protect Journalists, Safety of women and non-binary journalists on and offline. Website: https://cpj.org/campaigns/safety-women-nonbi-nary-journalists-online-offline/ Summary: Includes campaigns and advocacy for increased protection, safety resources, research and documentation of incidents. European Parliamentary Research Service Blog, Spotlight On Gender Equality In The Media And Digital Sectors, by Rosamund Shreeves, 2018. Website: https://bit.ly/35tGBj6 Summary: The article examines the EU media landscape through a gender lens, looks at the impact of gender imbalances and gender stereotyping, the actions that can be taken to address sexism in the media, and some best practices that have been adopted to counter gender stereotypes. African Women in Media & Fojo Media Institute. SourceHeran online database of African women experts across various industries in Africa and in Diaspora. Website: http://sourceher.africanwomeninmedia.com/ Ford Foundation, Gender Equity in the News Media: Analysis and Recommendations for Newsroom Leaders, by Ariel Skeath and Lisa Macpherson. Website: https://www.fordfoundation.org/media/5489/ grej-gender-media-report-102519.pdf Summary: The report analyses gender equity in the news media, identifies main challenges for women in the news media industry, and offers recommendations to change the "discriminatory culture" in newsrooms. GMMP2020 153 Who Makes the News? Free Press Unlimited, Equality and Inclusion Programme Website: https://www.freepressunlimited.org/en/themes/ equality-and-inclusion Summary: The programme supports "underprivileged, marginalised groups that are more likely to be discriminated against or to be forgotten in news coverage. These can be women, members of the LHBTI community, ethnic minorities and youth." Among other things, Free Press Unlimited works with partners to help empower female journalists by improving their conditions in newsrooms and investing in skills and opportunities of female journalists. Free Press Unlimited, Gender and Media Resource Guide Website: https://kq.freepressunlimited.org/themes/gen-der-equality/ Summary: The guide offers background information about gender and media, practical tools, and successful approaches to promoting gender equality in and through the media. Gender Ethics Compass, by World Association of Christian Communication (WACC) & Mediaspro (Geneva) Website: https://whomakesthenews.org/gender-eth-ics-compass/ Summary: This includes an interactive GPS designed for journalists and intended to provoke a critical reflection on the intersections between gender and journalistic ethics. Another GPS is designed for media professionals who create content, and aims to catalyze critical reflection on the intersections between gender concerns and journalist ethics. Global Investigative Journalism Network, GUN Guide: Resources for Women Journalists Website: https://gijn.org/gijn-guide-resources-for-wom-en-journalists/ Summary: A curated collection of resources on international and regional journalists' networks, safety, discrimination and harassment, mentors, grants and fellowships, female experts, awards, and investigative journalism. Harvard Business Review, Tackling the Underrepresentation of Women in Media, by Aneeta Rattan, Siri Chilazi, Oriane Georgeac, and Iris Bohnet Website: https://hbr.org/2019/06/tackling-the-underrepre-sentation-of-women-in-media "For over two years, journalists and producers across the BBC have been tackling the gender representation issue by rethinking whom they put in front of the camera, with the goal of achieving 50:50 gender representation every month. 500 BBC shows and teams have joined the so-called 50:50 Project. In April 2019,74% of the English-language programs that had been involved in 50:50 for a year or more reached 50%+ female contributors on their shows. How did an initiative that started in the newsroom (not the board room), by a white British man (not a D&I expert), come to thrive in an organization that has ongoing, public challenges related to gender equity (e.g., their gender pay gap)?" Informed Opinions, Gender Gap Tracker. Website: https://gendergaptracker.informedopinions.org/ Summary: "Informed Opinions' Gender Gap Tracker measures the ratio of female to male sources quoted in online news coverage across some of Canada's most influential national news media. By default, the graphs display the most recent week of data, but with a 3-day delay." International Association of Women in Radio and Television (IAWRT), Handbook on Working Toward Equality in the Media: The IAWRT and the Gender Mainstreaming Project, by Greta Gober, 2019. Website: https://bit.ly/3cKCMtO Summary: The handbook "showcases IAWRT members' experiences and best practices of working towards gender equality and women's positions in and through the media," and is divided into two parts: Actions to foster gender equality in media organizations and Actions to foster gender-fair portrayal in media content. GMMP2020 154 Who Makes the News? International Women's Media Foundation,The Missing Perspectives of Women in Covid-19 News, A special report on women's under-representation in the news media, 2020. Commissioned by the Bill and Melinda Gates Foundation and authored by Luba Kassova, director of international audience strategy consultancy AKAS Ltd. Website: https://www.iwmf.org/women-in-covidl9-news/ Summary: The report analyzed 57 million articles from 12,000 publications in six countries — India, Kenya, Nigeria, South Africa, the U.K., and the U.S. — in terms of their inclusion as sources and protagonists in news coverage, "including gender equality angles in reporting." It also looked at women's representation in newsrooms and leadership positions. Reflect Reality, Join the Global Movement to Source Women in the News, a project of United News, a multi-stakeholder coalition, led by Internews in collaboration with the World Economic Forum, with a mission to build trust and sustainability for news media around the world. Website: https://www.reflectreality.internews.org/ the-problem Summary: The project provides an overview of why it's important to increase women as sources in the news and identifies challenges that limit the inclusion of women as sources in the news. It offers resources and strategies to achieve gender parity in news sourcing and for diversifying sources beyond gender. And, it has examples of pilot projects that tested various paths toward gender equality in news sourcing. International Journalists' Network, Key takeaways for improving the representation of women in the news, by Edi Doychinova, Feb. 25, 2021. Website: https://ijnet.org/en/story/key-takeaways-improv-ing-representation-women-news Summary: The article summarises the key takeaways and quotes from the report, The Missing Perspectives of Women in Covid-19 News, A special report on women's under-representation in the news media, 2020. UNESCO, Selected Resources on Safety of Women Journalists. Website: https://en.unesco.org/themes/safety-journalists/ women-journalists/resources Summary: Offers resources on topics such as harassment, physical safety, online harassment, assistance, and reports and studies about women journalists' safety. Learning Resource Kit for Gender-Ethical Journalism and Media House Policy, is a collaboration between the World Association of Christian Communication (WACC) and the International Federation of Journalists (IFJ) to rectify gender disparities in news media content. Website: https://whomakesthenews.org/3655-2/ Summary: The kit aims to address the gender gap in news content and lack of self-regulatory mechanisms - where these do not exist - to confront gender bias. It is organized in two books that may be read independently of each other. Book 1 discusses conceptual issues pertaining to gender, media and professional ethics. Book 2 presents gender-ethical reporting guidelines on several thematic areas. UNESCO, UN Women and the International Federation of Journalists, Inside the News: Challenges and Aspirations of women Journalists in Asia and the Pacific. Website: https://www.ifj.org/fileadmin/images/Gender/ Inside_the_News_FINAL_040615_UNESDOC.pdf Summary: This research on media and gender in the Asia-Pacific region examines the experiences of women journalists in areas such as career progression, pay, cultural and social attitudes and challenges, and offers solutions to some of the issues. It also highlights "some real bright spots from which we can draw strong lessons," according to IF] Asia Pacific Deputy Director Jane Worthington GMMP2020 155 Who Makes the News? WAN-IFRA Women in News, Driving Equality in News Media Website: https://womeninnews.org/about-us/ Summary: WAN-IFRA Women in News partners with media organisations and individuals to close the gender gap in the news media. It believes that "balanced newsrooms, boardrooms and content are key to building resilient news organizations." Programmes include equipping women journalists and editors with the tools and support networks "to take on greater leadership positions within their organisations." It is currently working with more than 80 news organisations from 15 countries, in three regions, including Botswana, Kenya, Malawi, Rwanda, Somalia, Tanzania, Uganda, Zambia and Zimbabwe (WIN Africa); Egypt, Jordan, Lebanon and Palestine (WIN Arab Region); and Myanmar and Vietnam (WIN Southeast Asia). WAN-IFRA Women in News. Amplifying women's voices. A Gender Balance Guide For Media: http ://womeninnews. org/ckfinder/userfile s/file s/Gen-der%20Balance%20Guidebook_FINAL_RGB%20(l).pdf Women's Media Center, SheSource Experts Website: https://bit.ly/3qlEsov Summary: SheSource is an online database of women experts who have experience in media. Women+SourceList, by Brookings Institution Website: https://womenplus.sourcelist.org/ Summary: Women+ SourceList is a database of qualified women and underrepresented genders in technology who represent diverse backgrounds. "It is built on the principle that technology policy stands to benefit from the inclusion of the ideas, perspectives, and recommendations of a broader array of people. Its purpose is to aid journalists, conference organizers, and others in identifying and connecting with expert sources beyond those in their existing Rolodexes" 156