Comparing Climate Change Coverage in Canadian English and French-Language Print Media: Environmental Values, Media Cultures, and the Narration of Global Warming
Bibliographic record
Abstract
This article compares how climate change is presented in English- and French-language print media in Canada. In recent years, climate change has become an increasingly divisive issue, with the media playing a central role in the promotion of competing claims and narratives in the public sphere. Using concepts from environmental sociology and the sociology of journalism, we examine content from six English- and two French-language newspapers from 2007-2008 (N=2,245), and find significant evidence of both convergence and divergence across the language divide. Among the most significant findings are differences in how complexity is handled: English outlets present diverse coverage that is highly compartmentalized, while the French newspapers present a narrower range of coverage but with thematically richer articles that better link climate change issues to the realms of culture, politics, and economy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".