Social learning, innovative adaptation and community resilience to disasters: the case of flash floods in Bangladesh
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».