Social learning, innovative adaptation and community resilience to disasters: the case of flash floods in Bangladesh
Bibliographic record
Abstract
Purpose Existing literature on how social learning stemming from flood experience influences management and adaptation to flood-risks, and resilience-building is scant. In this context, the purpose of this study is to map the processes and examine the application of social learning in formulating coping measures and adaptation strategies in Bangladesh's wetland communities. Design/methodology/approach To bridge this research gap, conceptually, we formulated the Social Learning from Disasters (SLD) Framework to explain the process of social learning from flood experience and the mechanism of its influence on community resilience. Applying a qualitative research approach, the empirical investigation was carried out in the Fenarbak Union of Sunamganj District, Bangladesh. Using a participatory approach and qualitative techniques, the required primary data were procured. Findings The results of the study yielded three key findings: (1) social learning and memory have often enabled wetland communities to adopt diverse coping and adaptive measures in response to flash floods; (2) social learning-based actions have resulted in reduced flood-risk and enhanced community resilience to flash floods, especially when these actions were supported by both local and external innovations and (3) the aforementioned social learning stemmed primarily from first-hand experience of flash floods, which was shared via various collective learning platforms. Research limitations/implications The study followed a participatory methodology and the data were procured from two communities in the union level unit of Bangladesh. Therefore, generalization to apply to the larger context should be made with caution. Also, the study represents a cross-sectional study, and thus understanding of the long-term trend is not possible. Practical implications The findings of the study have direct and profound implications for local community-level disaster-risk planning. As there are serious deficiencies in documenting and preserving social learning for community resilience and development planning, this study offers a conceptual framework, along with empirical evidence, for transforming these lessons learned into practical actions for change. Social implications The findings of the study highlight the importance of social learning as a collective effort and provide empirical evidence of innovative adaptations to change. These results are critical to formulating societal strategies for disaster-risk management as well as to enhance community resilience. Originality/value Limited efforts have hitherto been made to determine (1) how the actual process of social learning from disaster shocks takes place, and (2) how innovative adaptation strategies lead vulnerable communities to take up social learning-based actions. Our research attempts to fill these knowledge gaps by providing an evidence-based account of community resilience-building responses to flash flood disasters.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".