Transmission of knowledge and social learning for disaster risk reduction and building resilience: A Delphi study
Bibliographic record
Abstract
Abstract The UN Sendai Framework recognized the need for making our communities safer and more resilient to disasters by shifting policy goals from “managing disasters” to disaster risk reduction (DRR) and building resilience. For DRR and building community resilience to disaster shocks, this study posits that social learning, a process of mutual development and sharing knowledge through iterative reflections on experience, is key to changing the conventional linear logic‐based, reactive framework into one based on learning‐by‐doing (adaptive management). Toward this end, a three‐round Policy Delphi process was pursued with a combination of 18 international DRR and SES (social–ecological systems) resilience scholars, practitioners, and public officials. Weak policy frameworks; operational, cultural and educational/training silos; and domination of technical knowledge were identified as major challenges in knowledge and learning transmission. Balancing technical knowledge with social science, and working toward transdisciplinary approaches and transformative practices should, therefore, be nurtured.
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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 | 0.048 | 0.040 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".