Disaster management policy changes in Bangladesh: Drivers and factors of a shift from reactive to proactive approach
Why this work is in the frame
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Bibliographic record
Abstract
Abstract In this paper, we argue that, while it is necessary to modify existing policy using the lessons learned from disaster events (i.e., reactive learning), this approach is insufficient on its own for dealing with ongoing and emerging climate‐induced disaster risks. Rather, we assert that policymakers must also adopt a proactive and anticipatory learning approach that would enable policy learning and policy evolution in the absence of a major disaster event. We examine drivers, actors, and processes of change in disaster‐management policy paradigms in Bangladesh. A longitudinal learning perspective is applied. We categorize disaster management (DM) policy regimes into three learning episodes: (i) reactive, (ii) transitional, and (iii) proactive. The roles of reactive and proactive learning in shifting DM policy paradigms within these learning episodes are particularly determined. Finally, five interrelated factors that triggered proactive policymaking are identified, which are: risk‐oriented policymaking; cross‐scale (i.e., lesson drawing and policy transfer) and cross‐level (i.e., from local, regional, and national experience) learning; participation of multiple stakeholders; research‐informed and knowledge‐based policymaking; and the presence of a strong advocacy group and a participatory policy process.
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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 | 0.000 | 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 it