Vulnerability based prioritization in disaster planning efforts: benefits and trade-offs
Bibliographic record
Abstract
Humanitarian logistics literature commonly uses Equity, Efficiency, and Effectiveness (3E) objectives. The equity objective strives to minimize differences among individual treatments by assuming that everyone is equally affected by a disaster. Efficiency measures aim to reduce the costs of aid programs, while effectiveness focuses on the quality of humanitarian aid, measured by factors such as response time or human suffering. The inherent assumption of 3E objectives is the homogeneity of the beneficiaries. However, it is essential to acknowledge that disasters disproportionately affect socioeconomically disadvantaged individuals. Vulnerable groups, including the low-income or marginalized, encounter unique challenges during disasters. Any measure assuming homogeneous demand will overlook the intersectionalities experienced by vulnerable communities. This paper introduces an alternative measure prioritizing vulnerable populations in disaster planning, aiming for a more inclusive and compassionate disaster management strategy. To compare the performance of this approach against the traditional 3E measures and analyze the associated trade-offs, we used the emergency assembly point allocation problem as a test case. We conduct computational analyses in synthetic and real-life instances using Istanbul’s neighborhood-level vulnerability and population. Our results demonstrate that vulnerability-based prioritization can achieve more inclusive results for vulnerable populations without significantly deteriorating 3E objectives and non-vulnerable population outcomes.
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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 | 0.009 | 0.026 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| 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".