Humanitarian Relief Supply Chain Performance Evaluation: A Literature Review
Bibliographic record
Abstract
<p>Nowadays small, medium and severe disasters are threatening our world. One of the important role players in alleviating these traits are humanitarian relief supply chains. The increasing number of disasters in our planet earth urges the humanitarian relief supply chains to focus on the assurance of safety of the victims. In order for this to occur, these supply chains should work effectively and efficiently. This can be possible through considerable evaluation of their supply chain performance. In this paper a literature review on supply chain performance evaluation in general and humanitarian relief supply chain performance in particular is presented. Previously conducted researches from the year 2000 until the present time have been reviewed. The works were categorized according to writers, publication year, publishing journal, technique utilized and objective intended. Then an analysis was made on humanitarian relief supply chain literature with respect to the publishing journals and the research technique applied. The result shows that humanitarian relief supply chain evaluation is almost an untouched area which needs further study. Recent supply chain management techniques can be applied for the improved performance of these supply chains. Based on this result, Supply Chain Operations Reference (SCOR), Fuzzy Logic System, and Artificial Neural Networks are found to be the areas which need further study.</p>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".