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Representations of Climate Change in Canadian National Print Media: The Banalization of Global Warming

2011· article· en· W1748288226 on OpenAlexaffabout
Nathan Young, Éric Dugas

Bibliographic record

VenueCanadian Review of Sociology/Revue canadienne de sociologie · 2011
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPolitical scienceHumanitiesPrint mediaClimate changeNewspaperArtLaw

Abstract

fetched live from OpenAlex

L'article ausculte des tendances longitudinales dans la couverture médiatique d'enjeux reliés aux changements climatiques au sein des journaux canadiens à distribution nationale. Nous constatons que cette couverture est devenu moins nuancée et davantage restreinte sur le plan thématique au fil du temps malgré le fait que les changements climatiques constituent aujourd'hui un enjeu scientifique et politique à polyvalence marquée. Bien que le contexte contemporain témoigne d'une intensité de couverture sans précédents, nous soutenons que les journaux canadiens à distribution nationale ont pris part d'un processus de «décontextualisation» des changements climatiques. Ainsi, nous notons un traitement infréquent de sujets reliés aux causes et impacts potentiels du phénomène ainsi qu'aux débats scientifiques pertinents. Simultanément, l'agencement superficielle de cet enjeu aux tiraillements politiques quotidiens et aux occurrences au sein du monde des d'affaires représente la tendance discursive dominante. This article examines longitudinal trends in media coverage of global climate change issues in Canada's national print media. While climate change is a multilayered scientific and political issue, we find that media coverage has become less nuanced and thematically narrower over time. Despite unprecedented levels of coverage in recent years, we argue that the Canadian national print media has been complicit in the “decontextualization” of climate change, whereby less attention is paid to issues of causation, scientific claims, and potential impacts, while more attention is granted to how climate change superficially intersects with everyday politicking and business issues.

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)0.002
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.045
Threshold uncertainty score0.287

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.007
Science and technology studies0.0110.005
Scholarly communication0.0090.003
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0120.000

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 head0.475
GPT teacher head0.437
Teacher spread0.038 · 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 designObservational
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

Citations68
Published2011
Admission routes2
Has abstractyes

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