Transformative learning and community resilience to cyclones and storm surges: The case of coastal communities in Bangladesh
Bibliographic record
Abstract
While it has been widely recognized that building community resilience to climate induced shocks requires learning processes at multiple societal levels, there has been limited research on the specific types of learning required at individual level to influence change and transformation at the community level. To determine how transformative learning and risk-mitigation actions shape community resilience to climate-induced disasters, we carried out a mixed-method empirical investigation on the southern coast of Bangladesh. We found that the relationship between transformative learning and resilience-building is complex, involving multiple social cultural-structural factors (e.g., beliefs, values, power structures), practical considerations (e.g., impact on livelihood, evacuation and relocation logistics), and cognitive factors. From our observations, we draw four general conclusions: i) local culture can constrain people's framing of risk and capacity for critical reflection, resulting in a deliberate denial and amnesia of past traumatic experiences; ii) learning alone cannot enhance resilience unless it is translated into action; iii) dependence on experiential learning can lead to the assumption that the severity of past disasters will not be surpassed, generating a false sense of security; and iv) the cultivation of forward-thinking attitudes coupled with innovative strategies, such as social networking, can successfully enhance resilience to climate-related disasters. Future policymaking aimed at building community resilience to climate shocks should therefore take into account cultural and individual cognitive barriers to transformative learning and attempt to remove structural barriers to translating learning into practical action.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".