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Record W2579987529 · doi:10.1080/17524032.2016.1275731

Media Access and Political Efficacy in the Eco-politics of Climate Change: Canadian National News and Mediated Policy Networks

2017· article· en· W2579987529 on OpenAlexafffundabout
Mark C. J. Stoddart, David B. Tindall, Jillian Smith, Randolph Haluza‐DeLay

Bibliographic record

VenueEnvironmental Communication · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsThe King's UniversityUniversity of British ColumbiaMemorial University of Newfoundland
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of MinnesotaNational Science Foundation
KeywordsPoliticsOpposition (politics)Political scienceNews mediaClimate changeInvisibilityPolitical economyVisibilityPublic administrationSociologyGeographyLaw

Abstract

fetched live from OpenAlex

We use a discourse network analysis approach to answer two questions about national news coverage of climate change policy debate in Canada during the period 2006–2010. First, what is the media visibility of actors relevant to policy development and advocacy on climate change? Second, given the political and economic context of climate policy-making in Canada, does greater or lesser media visibility reflect effectiveness in climate policy advocacy? Multiple interpretive frameworks characterize Canadian political discourse about climate change, with fragmentation between the federal government, opposition political parties, provincial governments, and environmental organizations. Contrary to expectations, environmental organizations had high levels of media visibility while the relative invisibility of fossil fuel corporations was notable in the media coverage of Canadian climate discussions. Our findings challenge optimistic accounts of the relationship between media power and political power, and suggest that media power does not necessarily translate to political efficacy.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.031
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.243

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.031
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0080.012
Science and technology studies0.0060.004
Scholarly communication0.0090.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.285
GPT teacher head0.440
Teacher spread0.155 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations31
Published2017
Admission routes3
Has abstractyes

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