Themes and patterns in print media coverage of wildfires in the USA, Canada and Australia: 1986–2016
Bibliographic record
Abstract
Background Media wildfire coverage can shape public knowledge on fire-related issues, and potentially influence management decisions, so understanding the content of its coverage is important. Previous research examining media wildfire coverage has primarily focused on either a single fire or issue, and provides little insight about the range of wildfire-related topics discussed in the media. Aims We aimed to assess wildfire topics covered in print media between 1986 and 2016 across the USA, Canada and Australia. Methods Machine learning-based automated content analyses were conducted to identify primary topics within news articles and to map relationships between topics. Key results News articles related to wildfire were clustered into four topic areas: Fire Response, Management, Environmental Conditions and Property Preparedness. Notable between-country differences emerge: the US media coverage focuses most on firefighting; Canadian coverage, climate change; and Australian coverage, preparedness. Conclusions Our results reveal that: (1) wildfire media coverage has increased over the past 30 years; (2) coverage is more varied than the common perspective, i.e. media continues to portray fires in a negative light; and (3) topic coverage varies significantly between countries. Implications These findings can help identify gaps in media coverage, and provide insights into critical topics, or relationships between topics, that may need additional emphasis in conversations about how to better learn to live with fire.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.009 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".