Evaluating Risk from Disasters to Improve Resilience: Lessons from Nigeria and South Africa
Bibliographic record
Abstract
Disaster risk is linked not only to the occurrence of severe hazards but also to the vulnerability factors that make disasters more likely when they do. Vulnerability is frequently associated with a set of fragilities, susceptibilities, and difficulties involving the lack of resilience of exposed human settlements in disaster-prone areas. It is also directly tied to social processes and governance deficits in disaster-prone areas. This study aimed at evaluating disaster risk over Nigeria and South Africa to improve resilience in the affected regions and to compare risk to assets in the two countries with the global average. This study reveals that Nigeria recorded losses to assets, economic resilience, and risk to well-being with about 0.12%, 48.3%, and 0.25% compared to the global average of about 0.63%, 61.12%, and 1.07%, respectively. More so, South Africa recorded losses to assets, economic resilience, and risk to well-being with about 0.24%, 55.13%, and 0.43% compared to the global average of about 0.63%, 61.12%, and 1.07%, respectively. Findings from both countries revealed that risks associated with disasters were high compared to the global average. This development requires urgent efforts to reduce risks in the two nations, as climate change continues to magnify natural hazards. Because protection infrastructure alone cannot eliminate risk, a more resilient strategy and inclusion of the outcomes in the planning and decision-making process are needed to critically break the cycle of disaster-induced poverty and vulnerability.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".