Is it climate change? Coverage by online news sites of the 2019 European summer heatwaves in France, Germany, the Netherlands, and the UK
Bibliographic record
Abstract
Abstract In 2019, several countries across Western Europe experienced record-breaking temperatures and heatwaves which, in some cases, reached temperatures of over 40 °C for three to four consecutive days during June and July. Extreme event attribution (EEA) studies show that anthropogenic climate change increased the likelihood of these events by at least three to ten times (with different results for different countries), and increased the temperature by 1.2 to 3.0 °C. The heatwaves resulted in more than 2500 deaths. Based on a content analysis of 267 articles taken from 20 of the most visited online news websites in four of the countries most affected by the heatwaves (France, Germany, the Netherlands, and the UK), we find strong variations between countries and media outlets in how much attention journalists pay to links between climate change and the heatwaves (the UK media the most, and politically left-leaning titles more than right-leaning ones); many different types of statements depicting the link but in general, the presence of accurate, science-based descriptions; a strong presence of EEA studies in the coverage; and more quotes from climate scientists than politicians and NGOs, with a minimal presence of climate change skeptics. These results contribute to our understanding of media coverage around extreme weather events in different countries and media outlets, and how such events might serve as opportunities for public engagement with climate change.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".