Examining contrasting influences of extreme weather experiences on individual climate activism
Bibliographic record
Abstract
Researchers have examined how extreme weather experiences influence climate change attitudes, beliefs, and behaviors, with mixed results. However, limited research has explored how extreme weather experiences may affect climate-related perceptions and behaviors among climate activists. Given the significant role activism plays in climate action, as well as frequent dropout and burnout among activists, it is important to understand to what extent, how, and why extreme weather may influence individual climate activism. This study explores reported influences of extreme weather experiences on climate perceptions and activism through interviews with 33 Australian adults who directly experienced bushfires and previously engaged in climate activism. All participants felt more vulnerable to climate change after experiencing bushfires. Fifteen participants (45%) increased their activism; 13 (39%) maintained the same activism level; and 5 (15%) decreased their activism. Participants who increased their activism sought to share their bushfire stories with news media, policymakers, and through artistic projects. Climate activism helped several participants cope with bushfire-related trauma, whereas several other participants reduced their activism because their experiences undermined self-efficacy (perception that one can act on climate change). These findings show the divergent ways individuals may respond to extreme weather experiences and have implications for climate action mobilization strategies.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".