Beyond fear: The role of emotions in disaster risk reduction in the face of climate change
Bibliographic record
Abstract
Most studies and policy in disaster risk reduction have focused on either what people lack (their vulnerability or their capacities to deal with risk (their resilience). Few studies and decision-making processes have focused on the role of emotions in informal urban settings. However, the results of a four-year study including interviews, three international workshops, and 24 community-led initiatives of risk reduction in Cuba, Colombia, and Chile, shows that emotions play a fundamental role in the design and planning of grassroots initiatives. Anxiety, pride, anger, uncertainty, and awe are crucial in risk-related agency. These emotions help building leadership and engagement and are decisive in establishing empathy, trust, and legitimacy—all which constitute the basis for change towards social and environmental justice. Phenomenology can help address connections between emotions, agency, and space. To succeed, risk response frameworks must recognize the interplay between emotions, behaviors, and politics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".