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Record W2110846451 · doi:10.1080/08941920.2015.1054569

Canadian News Media Coverage of Climate Change: Historical Trajectories, Dominant Frames, and International Comparisons

2015· article· en· W2110846451 on OpenAlexaffabout
Mark C. J. Stoddart, Randolph Haluza‐DeLay, David B. Tindall

Bibliographic record

VenueSociety & Natural Resources · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of British ColumbiaThe King's UniversityMemorial University of Newfoundland
Fundersnot available
KeywordsClimate changeNewspaperPolitical scienceNews mediaRhetorical questionPoliticsGlobeGovernment (linguistics)Media coveragePeriod (music)GeographyMedia studiesSociologyLaw

Abstract

fetched live from OpenAlex

We examine climate change news coverage from 1997 to 2010 in two Canadian national newspapers: the Globe and Mail and the National Post. The following questions guide our analysis: Why did the volume of climate change coverage rise and fall during the period? Focusing on the key period of 2007–2008, what kinds of issue categories, thematic frames, and rhetorical frames dominate the news discourse? Canadian news coverage of climate change is characterized by a series of peaks and troughs, combined with an overall increase in coverage. The volume of coverage appears to be primarily driven by national and international political events, more than by changes to national or global carbon emissions, or by other ecological factors. The Canadian news discourse about climate change is dominated by themes of government responsibility, policymaking, policy measures for mitigation, and ways to mitigate climate change.

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)0.003
metaresearch head score (Gemma)0.017
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 score0.065
Threshold uncertainty score0.473

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0320.056
Science and technology studies0.0060.002
Scholarly communication0.0070.003
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.243
GPT teacher head0.386
Teacher spread0.143 · 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

Citations57
Published2015
Admission routes2
Has abstractyes

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