Climate skepticism presence and changing climate journalism sourcing practices in the 2023 British Columbia wildfire coverage
Bibliographic record
Abstract
In 2023, Canada experienced one of the worst wildfire seasons in its history, with over 16.5 million hectares of land being burnt. These record-breaking wildfires required evacuations, posed physical and mental health risks, and required multiple levels of government emergency response. As wildfires have become increasingly intense, scientists have come to the consensus that climate change is a factor which is contributing to the worsening of these fires through increased global temperatures, changing seasonal patterns, and increased droughts. Media coverage of the wildfires combined event-driven reporting and climate journalism. Climate journalism has been an area of rapid change in journalism studies. Climate journalism has been moving away from previous ideas of objectivity and bothsidesism, and instead moving towards a subjective stance where climate change is not treated as a subjective ideal, but is instead treated as an objective fact, changing the way that different voices in climate journalism are represented. Climate journalism aligns with journalistic sourcing practices as certain groups, such as government officials, receive high media representation, and other groups experience less representation. This research project explored how different sources are represented in the 2023 Canadian wildfire media coverage, using both quantitative and qualitative analysis. It investigated which sources were the most likely to assert a connection between climate change and wildfires, and to understand how reluctance and skepticism are represented. Findings suggest that skeptic voice presence in media is decreasing, and that event-driven crisis reporting platforms a microcosm of dominant government sources. The results advance an understanding of how science communication is represented by sources, and which sources contribute to shaping the current representation of climate change in the media during a crisis event.
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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 | 0.010 | 0.035 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".