Community-based Disaster Management and Its Salient Features: A Policy Approach to People-centred Risk Reduction in Bangladesh
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The discourse of disaster management has undergone significant change in recent years, shifting from relief and response to disaster risk reduction (DRR) and community-based management. Organisations and vulnerable countries engaged in DRR have moved from a reactive, top-down mode to proactive, community-focused disaster management. In this article, we focus on how national disaster management policy initiatives in Bangladesh are implementing community-based approaches at the local level and developing cross-scale partnerships to reduce disaster risk and vulnerability, thus enhancing community resilience to disasters. We relied chiefly on secondary data, employing content analysis for reviewing documents, which were supplemented by primary data from two coastal communities in Kalapara Upazila in Patuakhali District. Our findings revealed that to address the country’s vulnerabilities to natural disasters, the Government of Bangladesh has developed and implemented numerous national measures and policies over the years with the aim of strengthening community-focused risk reduction, decentralising disaster management, developing cross-scale partnerships and enhancing community resilience. Communities are working together to achieve an all-hazard management goal, accepting ownership to reduce vulnerability and actively participating in risk-reduction strategies at multiple levels. Community-based disaster preparedness activities are playing a critical role in developing their adaptive capacity and resilience to disasters. Further policy and research are required for a closer examination of the dynamics of community-based disaster management, the role of local-level institutions and community organisations in partnerships and resilience building for successful disaster management.
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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 | 0.001 | 0.001 |
| 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 it