Reporting on the 2019 European Heatwaves and Climate Change: Journalists’ Attitudes, Motivations and Role Perceptions
Bibliographic record
Abstract
In summer 2019, several countries in Europe experienced unprecedented heatwaves. Two extreme event attribution (EEA) studies, which assess the role of climate change in extreme weather events, were published at roughly the same time as the heatwaves were taking place (June/August 2019). Building on a prior study of online news media coverage of the heatwaves, this study surveyed journalists from major news outlets in France, Germany, the Netherlands and the UK. Based on the responses of 42 journalists, we found a relative lack of knowledge about EEA studies but a high level of importance assigned to writing about the link between the heatwaves and climate change (e.g., likelihood or intensity); a relatively low number of specialist reporters vs. general reporters covering the heatwaves; a strong reliance on scientific experts as sources; no inclusion of climate change deniers; stronger role perceptions as educators than advocates; relatively little time and resource constraints on their reporting; and an overall tendency for the journalists to report more about climate change. The findings provide new insights into journalism practice and climate journalism in terms of the peculiarities and contextual factors that can influence coverage of extreme weather events and 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 | 0.010 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".