Discourse of Flood Management Approaches and Policies in Bangladesh: Mapping the Changes, Drivers, and Actors
Bibliographic record
Abstract
The fundamental processes of policy shifts emphasize how policy problems emerge and how policy decisions are made to overcome previous shortcomings. In Bangladesh, flood management policies may also have been driven by policy failures and flood-disaster events. In this context, we examined how policy shifts occurred in the country from 1947 to 2019 in areas of water management and flood prevention, control, and risk mitigation. To understand the nature of these policy shifts, we examined the evolutionary processes of flood management policies, the associated drivers, and the roles of key actors. Our findings reveal that policy transitions were influenced primarily by the predominance of the structural intervention paradigm and by catastrophic flood events. Such transitions were nonlinear due to multiple interest groups who functioned as contributors to, as well as barriers against, flood prevention policies. Policy debates over environmental concerns helped bring about a shift from a primary focus on structural intervention to a mixed approach incorporating various nonstructural interventions. Furthermore, our results suggest that the shifts in flood management policies have resulted in some degree of reliance on a “people-centered” approach rather than solely an “engineering coalition”, which emphasizes the pivotal role of community members in decision making and the implementation of flood policies and programs.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".