Social learning for enhancing social-ecological resilience to disaster-shocks: a policy Delphi approach
Notice bibliographique
Résumé
Purpose The plethora of contributions to social learning has resulted in a wide range of interpretations, meanings and applications of social learning, both within and across disciplines. However, advancing the concept and using social learning methods and tools in areas like disaster-shocks requires interdisciplinary consolidation of understandings. In this context, the primary focus of this paper is on the contributions of social learning to disaster risk reduction (DRR). Design/methodology/approach By applying a three-round policy Delphi process involving 18 purposefully selected scholars and expert-practitioners, the authors collected data on the meanings of social learning for two groups of professionals, DRR and social-ecological resilience. The survey instruments included questions relating to the identification of the core elements of social learning and the prospects for enhancing social-ecological resilience. Findings The results revealed strong agreement that (1) the core elements of social learning indicate a collective, iterative and collaborative process that involves sharing/networking, changes in attitudes and knowledge and inclusivity; (2) social learning from disasters is unique; and (3) linkages between disciplines can be built by promoting interdisciplinarity, networks and knowledge platforms; collaboration and coordination at all levels; and teaching and practicing trust and respect. Social learning is useful in preparing for and responding to specific disaster events through communication; sharing experience, ideas and resources; creating synergies for collective action and promoting resilience. Research limitations/implications The policy Delphi process involved a limited number of participants to control the quality of the data. To the best of the authors’ knowledge, this paper is the first of its kind to identify the core elements of social learning, specifically, in the disaster-shock context. It also makes significant contributions to the interdisciplinary integration issues. Practical implications The practical implications of this study are related to pre-disaster planning and mitigation through the application of social learning on disaster-shocks. Social implications The social implications of this study are related to valuing social learning for the improvement of disaster planning, management, and policy formulation and implementation in reducing disaster risks. Originality/value The study provides a consensus view on the core elements of social learning and its role in DRR and resilience building. Relevant to all stages of DRR, social learning is best characterized as a collective, iterative and collaborative process. It can be promoted by enhancing networking and interdisciplinarity.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
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 | 0,001 | 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 | 0,001 | 0,001 |
| Science ouverte | 0,001 | 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 ».