Social learning-based disaster resilience: collective action in flash flood-prone Sunamganj communities 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
Despite widespread recognition that social learning can potentially contribute toward enhancing community resilience to climate-induced disaster shocks, studies on this process remain few and far between. This study investigates the role of local institutions (formal, informal, and quasi-formal) in creating learning arenas and translating social learning into collective action in flash flood-prone Sunamganj communities in Bangladesh. We follow a Case Study approach using qualitative research methods. Primary data were collected through 24 key informant interviews, 10 semi-structured interviews, six focus-group discussions, and two participant observations events. Our results reveal that the diversity and flexibility of local-level institutions creates multiple learning platforms in which social interaction, problem formulation, nurturing diverse perspectives, and generating innovative knowledge for collective action can take place. Within these formal and informal learning arenas, communities’ desire and willingness to be self-reliant and to reduce their dependency on external funding and assistance is clearly evident. Social learning thus paves the way for institutional collaboration, partnership, and multi-stakeholder engagement, which facilitates social learning-based collective action. Nurturing institutional diversity and flexibility at the local level is therefore recommended for transforming social learning into active problem-solving measures and to enhance community resilience to disaster shocks.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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