The Role of Newspapers in Environmental Policy Change: Media Framing of Climate Change Events in British Columbia and Alberta
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".