Loss-and gain-framed messages alter climate emotions, but not behaviour across 26 countries
Bibliographic record
Abstract
Efforts to communicate climate urgency often hinge on whether messages emphasise gains (e.g., preserving natural environments) or losses (e.g., losing natural environments). In an online between-participant experiment across 26 countries (n = 11,934), we examine how message frames influence the perceived severity of climate change impacts, information-seeking intention, and information-seeking behaviour. The loss-framed message resulted in higher perceived severity of climate change (β = 0.07, p < 0.001) compared to the gain-framed message. This effect is driven by emotions: loss-framed messages primarily elicit anxiety (β = 0.57, p < 0.001), while gain-framed messages boost hope (β = -0.86, p < 0.001). However, framing had a minimal effect on information-seeking intention (β = 0.03, p < 0.001) and did not alter actual information-seeking behaviour. Overall, loss-framed messages increase climate urgency, and gain frames provide emotional benefits without lowering urgency, indicating that effective climate communication needs to balance the two.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".