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Record W6996138371

The Role of Newspapers in Environmental Policy Change: Media Framing of Climate Change Events in British Columbia and Alberta

2020· article· en· W6996138371 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship at UWindsor (University of Windsor) · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicClimate Change Communication and Perception
Canadian institutionsnot available
Fundersnot available
KeywordsFraming (construction)NewspaperClimate changeNarrativePoliticsNews mediaEnvironmentalismPublic policy
DOInot available

Abstract

fetched live from OpenAlex

How does the media frame wildfires in BC and Alberta? In two provinces with different climate change policies and economic concerns, does the media mirror political beliefs? Policy in Canada is determined by the opinions and beliefs of individuals, the information they access, and what people believe to be the causes of problems. The level of attention to policy problems, the framing strategies used, and the presented scope of possible policy solutions by the media is important for defining the problem policy makers will have to solve. The Narrative Policy Framework, created in America, identifies narrative framing strategies and measures the role of media in policy change. This research builds on the literature of existing case studies in the United States to test this framework on extreme weather incidents in BC and Alberta. In the Canadian context, the political landscape varies between provinces and over time, as eras of environmentalism tend to alternate with times of economic hardship.\nBy looking at British Columbia and Alberta wildfires, this research examines how the media frames these stories to determine whether they are seen as climate change incidents or not and contributes to the understanding of how media framing compares between Canadian jurisdictions and in contrast to the American state-level examples.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.672
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.106
GPT teacher head0.297
Teacher spread0.191 · 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 teacher head, 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

Citations0
Published2020
Admission routes1
Has abstractyes

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