Insights from vulnerability-driven optimisation for humanitarian logistics
Bibliographic record
Abstract
Natural disasters and the vulnerability of a population go hand in hand. We cannot understand the level of a disaster without grasping the extent of people�s vulnerability. But how can we ensure that humanitarian assistance is driven by people�s vulnerability when the lack of resources makes it impossible to support all those that need it? This study thus contributes to this line of research by enhancing our understanding of how we can �put the reality of the most vulnerable people first� (cf. Chambers 1995). For this purpose, we examine two experiences that have proposed to incorporate vulnerability concerns into the planning and optimisation of humanitarian logistics operations. The first experience relies on a very popular composite indicator called Social Vulnerability Index (SoVI) to build enhanced response capacity in more vulnerable areas. The second experience is built upon a poverty measure called Foster-Greer-Thorbecke (FGT) to identify the groups that potentially need the most relief aid supply and to help devising allocation plans in compliance with people�s income. These two experiences reveal that in most cases targeting more vulnerable areas increases their level of access to relief aid goods without greatly compromising the relief service levels of less vulnerable areas.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".