The IPCC in the hybrid public sphere: divergent responses to climate mitigation solutions in mainstream and social media
Bibliographic record
Abstract
Abstract In April 2022 the Intergovernmental Panel on Climate Change (IPCC) published its report on the mitigation of climate change, which included detailed discussion of the wide range of solutions at the personal, societal and governmental level needed to reduce emissions. The report generated extensive societal debate and interest in mainstream and social media. Using manual text analysis, we examined 66 articles on more than 20 popular English-language online news sites in the UK and USA and the 56 most shared posts or tweets on Facebook and Twitter about the report. First, we found that the mainstream media faithfully reported the IPCC’s priority messages, and often included the IPCC’s own critique of some solutions, such as Carbon Dioxide Removal, as compared to critiques from other sources. The coverage represented a sharp break with the historical tradition of focusing on the negative, disaster-focused impacts of climate change in favor of more positive, solutions-based reporting. Secondly, in sharp contrast, many of the most widely-shared social media posts did not closely follow the IPCC’s main messages. Less than a quarter of the posts contained the summary message of the report, and about half mentioned no solutions at all. Instead they focused on the direness of the situation and the urgency with which action needs to be taken. Finally, there was a very low presence of voices from the organized climate countermovement, who often question the need to take far-reaching and rapid mitigation action. We discuss the significance of our results for future research and for practical action.
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 | 0.025 | 0.071 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".