Beyond Institutional Silos: Rethinking Multilevel Disaster Risk Governance in Africa a Decade into the Sendai Framework Implementation
Bibliographic record
Abstract
Abstract Ten years after the adoption of the Sendai Framework for Disaster Risk Reduction 2015–2030, disaster risk governance remains one of its most ambitious yet unevenly implemented priorities, particularly in African contexts. While Priority 2 articulates a comprehensive vision of inclusive, coordinated, and multisectoral governance, many African countries continue to operate without updated disaster legislation or coherent institutional frameworks. This study critically examined how Priority 2 has been interpreted and operationalized in five African countries—Kenya, Nigeria, Egypt, Namibia, and the Democratic Republic of Congo—drawing on qualitative document analysis and a thematic framework derived from the Sendai Framework governance dimensions. The study found partial alignment with Sendai Framework’s aspirations, especially in legal reforms, multilevel planning, and stakeholder engagement in countries like Kenya and Namibia. However, persistent gaps remain in integrating disaster risk reduction into sectoral policies, institutionalizing participation, and ensuring transparency and accountability. The Sendai Framework’s emphasis on technocratic coordination and universal governance models often overlooks power dynamics, historical inequalities, and informal institutional realities, limiting its transformative potential. Participation is frequently symbolic rather than substantive, and risk is treated as a technical variable rather than a product of structural vulnerability. These findings underscore the need to move beyond compliance-driven governance models toward more context-sensitive, adaptive, and justice-oriented approaches. As global risk landscapes evolve, the post-2030 agenda must prioritize institutional learning, power redistribution, and inclusive decision making.
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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 | 0.032 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".