Four core principles to reconcile sociocultural conditions and disaster risk reduction in pursuit of community resilience
Bibliographic record
Abstract
As environmental risks, particularly climate change, exacerbate vulnerabilities, Disaster Risk Reduction (DRR) has increasingly prioritised community protection. However, communities’ unique and contextual nature often renders top-down risk management efforts unsustainable or ineffective. To address these limitations, the community-based approach (CB) has emerged as a promising alternative. It is grounded in four interdependent principles: local participation, valuing diversity and inclusivity, integrating local and indigenous knowledge, and building local capacities for greater autonomy. Each of its principles benefits each other through a dynamic of interconnection and interdependence, which collectively ensure that DRR strategies are tailored to each community's specific needs, strengths, and sociocultural contexts. By promoting decentralised decision-making, participatory governance, co-production, and social learning, the CB approach aligns DRR efforts with local realities, making them more sustainable and effective. Although challenging to implement due to resource constraints and political dynamics, CB remains a vital pathway for building long-term community resilience in the face of evolving environmental risks. This paper provides a comprehensive framework for aligning DRR strategies with sociocultural conditions, offering practical insights and actionable recommendations to enhance community resilience.
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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 | 0.015 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.039 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".