Lessons from the Implementation of the Sendai Framework for Disaster Risk Reduction from Latin America and the Caribbean
Bibliographic record
Abstract
Abstract Over the past decade, Latin America and the Caribbean (LAC) have made progress in implementing the Sendai Framework for Disaster Risk Reduction 2015–2030 (SFDRR). Still, significant challenges remain in assessing its impact. The region’s high levels of inequality and vulnerability to disasters continue to hinder the effectiveness of disaster risk reduction (DRR) efforts. This article emphasizes the importance of a multi-stakeholder approach in SFDRR implementation, particularly the role of regional intergovernmental organizations (IGOs) and networks that promote collaboration among civil society, the private sector, Indigenous peoples, persons with disabilities, youth, and marginalized groups. Despite government efforts to integrate SFDRR into national policies, gaps in stakeholder engagement, resource allocation, and governance limit DRR effectiveness. The article underscores the value of co-production for involving communities to contribute to designing DRR strategies that address their specific needs. Co-produced strategies can bridge the gap between high-level policies and practical solutions by leveraging local knowledge and fostering partnerships. The review of regional networks’ efforts highlights the central role of IGOs in coordinating DRR strategies. These networks help create innovative solutions that empower communities. The article advocates for thinking about the subsequent phases post-SFDRR, drawing on the lessons from the regional networks and calls for more strategic collaborations and experimentation as a model to promote effective governance of DRR by engaging multiple stakeholders to design and pilot locally-driven solutions that can accelerate the implementation of the priorities of the SFDRR to reduce disaster risks in LAC through collaborations that build capacity through action and ensure meaningful engagement.
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 | 0.024 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.009 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.003 | 0.013 |
| Research integrity | 0.004 | 0.007 |
| 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".