Have we Reached a “Tipping Point” in Climate Change Reporting? How Mainstream Newspapers Cover Heatwaves
Bibliographic record
Abstract
This paper investigates the evolution of media coverage on heatwaves from 2010 to 2022 focusing on six countries (UK, USA, India, Australia, Canada, and China) through analysis of 27,964 newspaper reports. By employing content analysis and named entity recognition, we examine the volume of heatwave-related reports, their connection to climate change, and the incorporation of environmental science within media narratives. We explain our findings with reference to the concept of a dynamic or mobile public sphere. We identify three types of diffusion in the reporting of climate change in public spheres, which we term temporal diffusion, geographical diffusion, and political diffusion. These concepts illustrate the spread of ideas across time, space, and ideology. These shifts are characterised by an escalating media focus on heatwaves over time and the increasing association between heatwaves and anthropogenic climate change, a diffusion from countries in the Global North to the Global South, and a broadening attention from predominantly left-wing media across a wider ideological spectrum. This study contributes to our understanding of how journalistic practice, accumulated media attention, and environmental science can lead to significant shifts in transnationally linked public spheres, encouraging broader societal recognition of and engagement with climate change issues.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.001 | 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".