Magnifying Focusing Events: Global Smoke Plumes and International Construal Connections in Newspaper Coverage of 2020 Wildfire Events
Bibliographic record
Abstract
As climate policy focusing events, wildfires are distinct from hurricanes, floods, and tornados because they also result in the release of massive smoke plumes that contribute to the concentration of atmospheric carbon. However, unlike melting glaciers, wildfires may be easier to dismiss as individual acts of human error, spontaneous acts of mother nature, and/or necessary ecological processes of agricultural renewal. This paper presents a mixed-methods analysis of 150 international and domestic English language newspaper articles related to wildfire events occurring in Australia, Canada, Germany, Greece, Italy, Spain, the United Kingdom, and the United States during the year 2020. The analysis examines how news coverage of wildfire events might focus or diffuse attention to international climate policy and anthropogenic global warming. The quantitative findings provide evidence to suggest that 30% of wildfire coverage is attributed to climate change. However, qualitative analysis suggests that climate change is acknowledged as a blame frame that is often only inferentially attributed to anthropogenic origins. The mixed-methods analysis finds that only 6% of news coverage related wildfire events to emission contributions. The analysis of these exemplar articles suggests that the international travel of wildfire smoke may serve as a focusing event from which to emphasize wildfires as both a consequence of and contributor to, global warming. Findings indicate that environmental coalitions and scientific experts’ engagement with the press are integral to creating frames that link the increasing frequency, duration, and range of wildfire events to climate policy needs.
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 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 | 0.006 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.003 |
| 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".