Analysis of the Impact of Fragmented Coordination During Natural Disaster Responses on Access to Drinking Water Following Cyclone Idai - Beira Case Study
Bibliographic record
Abstract
Globally, disasters affect every domain of human activity and cause devastating losses across the \nhuman, economic and environmental domains. While they are extremely difficult to predict and \nprevent, the global society is, in principle, more than capable of mitigating many of the most \nsevere consequences. Worryingly, while the prevailing efforts often fall short future disaster \nimpacts are likely to become even less effective because of several compounding factors. \nAmong the various novel perspectives that emerged to resolve the shortcomings of current \ndisaster risk management efforts, a promising insight is offered by the lens of institutional \nvulnerability. This research provides a strong rationale for the recognition of institutional \nvulnerability as an insightful tool in addressing the most common areas of criticism around the \nexisting DRM approaches. These finding are based on a case study analysis that centers around \nthe 2019 Cyclone Idai and its impact on the City of Beira. \nThe research reveals that institutional vulnerability is both a factor in the progression of \nvulnerability, but also a key mitigating variable in the development of the disaster. By expanding \nthe analysis of the progression of vulnerability to include institutional vulnerability, it is possible \nto create a more comprehensive account of how a disaster unfolds and recognize the key role that \ninstitutional vulnerability has in amplifying or mitigating the disasters.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".