Paris school and scientific knowledge
Petr Ocelík
ESS427 / MEBn4001 / MEB427
5th December 2019
Outline
• field and capital: refresh
• scientific/expert field and capital
• scientific/expert knowledge de/securitization mechanisms
• case study: coal policy field in the Czech Republic
Bourdieu: field
• field: a relatively autonomous, hierarchically organized social space
within which transactions, interactions, events etc. in a particular
sphere of social life take place
• analogy: a “sports field” or a chess board
• there are different kinds of fields: political, military, organized crime,
academia, art, medical, bureaucratic, scientific, security experts etc.
• each field operates according to its own logic (nomos)
Bourdieu: capital
• the structure of the social world is conditioned by the distribution of
various forms of capital
• capital: an accumulated labor that enables actors to influence their
position and position of others within a given field
• economic capital: an accumulation of money, assets, property rights
• cultural capital: an accumulation of knowledge, abilities, qualifications etc.
• social capital: an accumulation of social ties to potential resources
• symbolic capital: legitimated form of the other capitals
How would you define expert field and capital?
Scientific/expert field and capital
• main stake: scientific competence
particular agent’s socially recognized capacity to speak and act legitimately
in scientific matters (Bourdieu 1975: 19)
• scientific/expert capital: a symbolic capital of recognition in the form of
scientific authority (Bourdieu 2004)
• actors produce scientific/expert knowledge
• relevance for policy-making (Boswell 2008) and de/securitization processes (Berling 2011)
Who produces scientific/expert knowledge?
Producers of scientific/expert information
• Production of scientific/expert knowledge is not limited to scientific institutions
• Academia
• Public authorities: government departments, administrative agencies, and
political parties
• Think-tanks and generally NGOs
→ grey literature: research produced outside established distribution channels
(academic publishing houses) Wagner et al. n.d.
Scientific knowledge re/production
• science influences what can be said and what not:
the non-politicized has no language; it is what we know without knowing
that we know it (Berling 2011: 391)
• scientific or expert knowledge: a privileged form (Berling 2011)
• legitimation
• mobilization
• objectification
Legitimation
• legitimation: scientific knowledge (1) strengthens authority of speaker
and (2) certifies related policy/security decisions
• privileged form of knowledge
• often position of “neutral arbiters”
➢ scientific field influences status of a de/securitizing actor
Mobilization
• mobilization: scientific knowledge and facts used as discursive resources
to enhance de/securitization appeals
• de/securitization appeals backed (or even driven) by scientific evidence
➢ the goal is often to win debate/controversy and close it (objectification)
Non-knowledge: “conscious or unconscious, concrete
or theoretical, it can signify willful ignorance or an
inability-to-know.” (Beck 2009: 123)
Objectification
• objectification: issue defined as a matter of scientific inquiry or necessity
• black-boxing: a specific system or mechanism is understood in terms of
in/out-puts
• closing down debates/controversies: establishes a doxic practice
➢ influences external dynamics of de/securitization
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Case study: climate skepticism in Czechia
̶ Media discourse as crucial layer of subsystem politics (Broadbent et al. 2016; Kukkonen et
al. 2017; Leifeld 2013)
̶ Involves diverse actors that compete through agenda-setting (McCombs and Shaw 1972)
and (counter)framing (Boykoff 2011)
➢ How does presence of climate skepticism evolves over time?
➢ What, if any, counter-framing strategies are used by skeptics?
Focus on title pages: issue salience and visibility (Schuck et al. 2013)
23/10/2019, COMPON Workshop, University of Bern
19
Counter-framing strategies
̶ Benford & Hunt 2003 define four counter-framing strategies:
1. Problem denial
2. Counter-attribution
3. Counter-prognoses
4. Attacks on collective character
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Expectations
E1. Prominent position of president Václav Klaus (Vávra et al. 2013; Vidomus
2013, 2018)
E2a. Shift from epistemic skepticism to response skepticism (Capstick & Pidgeon
2014)
E2b. Shift from problem denial/counter-attribution to counter-prognosis
(Benford & Hunt 2003)
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Data
̶ 4 major daily newspapers
̶ Query: (climate <AND> change) <OR> (global <AND> warming) in fulltext
̶ Period: 2009-2018
➢ Total corpus: 6012 articles (uncleaned)
➢ Sample: 303 documents (title page contents) with 800 coding units (70 codes and 240 actors)
̶ Coded by two independent coders in Discourse Network Analyzer (Leifeld 2013)
̶ Krippedorf Alpha = 0.92
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actors other concepts skeptic concepts
actors x concepts subtract network LC
negative ties positive ties
actors congruence network LC
2009
CC priority agenda 16
sociopol. threats 16
socioecon. costs 14
int. negotiations 11
dev. countries fund 9
Klaus as outlier 8
int. efforts crucial 8
mitigation necessary 7
higher targets 6
Copenh. failure 4
TOP10 concepts frequencies (N = 168)
23
actors x concepts subtract network LC actors congruence network LC
2009
CC priority agenda 16
sociopol. threats 16
socioecon. costs 14
int. negotiations 11
dev. countries fund 9
Klaus as outlier 8
int. efforts crucial 8
mitigation necessary 7
higher targets 6
Copenh. failure 4
TOP10 concepts frequencies (N = 168)
actors other concepts skeptic concepts negative ties positive ties
24
drought 13
impacts manag. 12
gen. neg. impacts 12
extreme weather 9
biodiv. loss 6
adap. infrastructure 4
soc. awareness 4
inst. adap. 3
sources 3
temperature ind. 3
actors x concepts subtract network LC actors congruence network LC
2018
TOP10 concepts frequencies (N = 88)
actors other concepts skeptic concepts negative ties positive ties
25
Presence of climate skepticism
VK’s last full year in
the office
26
Skeptics’ counter-framing strategies
VK’s last full year in
the office
27
Conclusions [preliminary]
̶ E1. Prominent position of president Václav Klaus
̶ A dominant figure between 2009-2011, afterwards expertization of discourse
̶ E2a. Shift from epistemic skepticism to response skepticism
̶ Both skepticism types move similarly, after 2013 marginal
̶ Response skepticism overall more present
̶ E2b. Shift from problem denial/counter-attribution to counter-prognosis
̶ Attacks on collective character the only relevant counter-framing strategy
̶ Primarily ideological not epistemic focus
Case study: coal policy field in Czechia
⚫ the lignite production accounts for 46% of TPES and 51%
of electricity mix
⚫ it is concentrated in the Sokolov Basin and the North
Bohemian Basin
⚫ the territorial mining limits has been established by
government decree in 1991
stakes:
⚫ a lifting of “the limits” became a key issue in the energy
policy since then
⚫ transition pathway to decarbonized economy very much
depends on the future of coal
03/05/2018, SMI Seminar Series, University of Queensland
Advocacy coalitions perspective
• policy actors (typically) cannot achieve their objectives on their own
• public policies are shaped by interactions and coalition formation where actors
share information as well as resources, and exercise power against rival coalitions
(Stoddart & Tindall 2015)
• the advocacy coalition perspective defines coalition as a group of actors that:
(1) share policy beliefs; and
(2) engage in mutual coordination
1. Shared policy core beliefs
• normative assumptions on how specific policy
field ought to be organized
• captured by 4 Likert-type scales:
• economy: costs/benefits of coal, regional
development
• environment: environmental and health impacts
• policy: future of coal in energy mix, question of
the mining limits
• process: trust among key actors, regulatory
framework
2. Factions
• cohesive parts of the field/network =
• groups of actors that are connected more
among themselves than with others
Data collection
• organizational actors involved in coal policy field
sector responded total response rate (%)
central and regional governance 16 16 100
central and regional political parties 16 18 89
environmental non-governmental organizations 8 9 89
research organizations 14 16 88
professional associations & trade unions 3 7 43
industry 11 17 65
total 68 83 82
Note: includes partial responses
Data collection
• the survey instrument (a self-administered online questionnaire) collects data on
attribute variables: (1) policy core beliefs and (2) network ties
network tie
political influence (PI) network directed binary tie
expert information (EI) exchange network directed binary tie
political cooperation (PC) network directed binary tie
Results: usual suspects
• The Industry Coalition:
• dominant coalition with superior resources and
direct access to decision-making
• huge vested interests that go against transition
• consists of 17 organizations:
• 3 political parties (central)
• 2 political parties (Ústí region)
• 2 state agencies (central)
• 1 state agency (Ústí region)
• 2 regional agencies (Ústí region)
• 6 companies
• The Environmental Coalition:
• minor coalition reliant on its relational capacity
and expert knowledge
• consists of 18 organizations:
• 8 ENGOs
• 2 state agencies (central)
• 2 political parties (central)
• 6 research organizations
Results: usual suspects
the scales range between <0,1> ; where 0 = very strong pro-coal position, 1 = very strong anti-coal position
different colors/letters indicate statistically significant difference between the groups at p < 0.05
Expert information: Tell me I am right?
• Expert information is crucial for management of complex socio-technical systems
(Giddens 1990) – includes evidence-based policy-making
• Its importance increases under conditions of uncertainty (Cairney et al. 2016)
• Two opposing approaches:
• Technocratic governance: exp info abrades ideological differences and “builds bridges”
• Expertise politics: exp info is used to defend ideological positions of their
holders/providers
Block modeling
• Block model (BM) is a simplified representation of a network (White et al. 1976):
• Groups of nodes with similar relations to others (blocks)
• Patterns of relations among blocks (social roles)
Expert information: Tell me I am right?
• Coalitions identified based on political cooperation network and shared policy core beliefs
Blocked density matrix: expert information
Adj R^2 = 0.102
Bolded cells indicate significant differences
from the average (network density = 0.173)
Expert information: Tell me I am right?
• expert information is crucial for management of complex socio-technical systems
(Giddens 1990)
• evidence-based policy-making
• its importance increases under conditions of uncertainty
• technocratic governance: exp info abrades ideological differences and “builds bridges”
• expertise politics: exp info is used to defend ideological positions of their holders/providers
• more than 2.5 times more likely to exchange expert information within advocacy
coalitions than between the coalitions
➢ contributes to polarization and limits policy change by learning