Humanitarian Organizations in International Disaster Relief: Understanding the Linkage Between Donors and Recipient Countries
Bibliographic record
Abstract
Abstract This study explores how humanitarian organizations (HOs) link donors and recipients in a disaster relief coordination mechanism. Based on an analysis of real data collected from the financial tracking service, our results show that disaster assistance through the HO channel greatly exceeds the funding delivered by the non-HO channel. The severity of the disaster is positively correlated with the involvement of HOs. Disaster-stricken countries that belong to the Non-Aligned Movement receive more assistance through the HO channel. The recipients with less international trade may attract more HO-channel funding, but higher international tourism expenditures also may result in more HO-channel funding. We also found that the determinants of the disaster relief coordination path vary greatly in terms of trade openness, political regime, and geographic factors. Based on the analysis of the primary humanitarian relief supply chain, the results show that some countries prefer to donate through large international HOs (e.g., Japan and Canada), and other countries favor national level organizations (e.g., the UK and the USA). Finally, to improve the efficiency of international disaster relief, the paper suggests a coordination platform that involves the main donors, frequent recipients, HOs, and a Global Information Network that can assist in coordinating disaster relief activities.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".