Improving the feedback loop between community‐ and policy‐level learning: Building resilience of coastal communities in Bangladesh
Bibliographic record
Abstract
Abstract Building community resilience has been widely recognized as a learning process at multiple societal levels, yet few prior studies have examined the feedback loop between community‐ and policy‐level learning. Following a qualitative research approach, we document experiential and transformative forms of learning from coastal cyclones in Bangladesh that help local community and their institutions mitigating the impact of cyclonic shocks and recovering from disaster‐related losses, both in the shorter and longer term. This study discovers that such community‐level learning (when scaled‐up) as well as learning from policy failure significantly enhanced programmatic interventions, which in turn enhanced community resilience to cyclones and future disasters. However, this feedback loop can be attenuated by multiple factors, such as lack of attention to community‐level learning by policy/decision makers in non‐disaster settings and the presence of a strong vested interest group, may impede learning‐based policy instrumentation. Boundary spanners or organizations can significantly improve the feedback loop, thus enhancing community resilience and improving policy.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".