Social learning for enhancing social-ecological resilience to disaster-shocks: a policy Delphi approach
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.126 | 0.066 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.008 | 0.010 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".