Transmission of knowledge and social learning for disaster risk reduction and building resilience: A Delphi study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it