Fishers' responses to tropical cyclones in coastal Bangladesh
Bibliographic record
Abstract
Coastal communities in general have been studied in the context of disaster. However, the specific responses of fishing communities to tropical cyclone events remain relatively under-explored in the disaster science literature. This study investigates fishers' responses to tropical cyclones and various factors that impact behavioral decisions on whether to go to a cyclone shelter. The findings suggest that fishers' coping mechanisms involve securing daily necessities through their initiatives, reliance on kinship relations and obligations, diversification of livelihoods, intensification of fishing, and engagement in social networking and environmental management. The findings suggest that approximately half of the participants refrained from seeking refuge in cyclone centres for various reasons. Crucially, the socio-economic and occupational status of fishing communities significantly influenced their reluctance to comply with evacuation orders. Recognizing non-compliance with evacuation orders is a leading factor in cyclone-related human fatalities and addressing and mitigating non-compliance is essential. Integrated and comprehensive approaches, including cross-sector cooperation, will be needed for effective disaster risk management strategies within small-scale fishing communities. • Fishers' responses to tropical cyclone events have remained relatively less studied within the disaster science literature. • Socio-economic and occupational status significantly influenced fishers' reluctance to comply with evacuation orders. • Integrated and comprehensive approaches, will be needed for effective disaster risk management for fishing communities.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".