A multi-criteria vertical coordination framework for a reliable aid distribution
Bibliographic record
Abstract
Purpose: This study proposes a methodology that translates multiple humanitarian supply chain stakeholders’ preferences from qualitative to quantitative values, enabling these preferences to be integrated into optimization models to ensure their balanced and simultaneous implementation during the decision-making process.Design/methodology/approach: An extensive literature review is used to justify the importance of developing a strategy that minimizes the impact of a lack of coordination on humanitarian logistics decisions. A methodology for a multi-criteria framework is presented that allows humanitarian stakeholders’ interests to be integrated into the humanitarian decision-making process.Findings: The findings suggest that integrating stakeholders’ interests into the humanitarian decision-making process will improve its reliability.Research limitations/implications: To further validate the weights of each stakeholder’s interests obtained from the literature review requires interviews with the corresponding organizations. However, the literature review supports the statements in this paper.Practical implications: The cost of a lack of coordination between stakeholders in humanitarian logistics has been increasing during the last decade. These coordination costs can be minimized if humanitarian logistics’ decision-makers measure and simultaneously consider multiple stakeholders’ preferences.Social implications: When stakeholders’ goals are aligned, the humanitarian logistics response becomes more efficient, increasing the quality of delivered aid and providing timely assistance to the affected population in order to minimize their suffering.Originality/value: This study provides a methodology that translates humanitarian supply chain stakeholders’ interests into quantitative values, enabling them to be integrated into mathematical models to ensure relief distribution based on the stakeholders’ preferences.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".