Community-based Disaster Management and Its Salient Features: A Policy Approach to People-centred Risk Reduction in Bangladesh
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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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 | 0.006 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| 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".