Food Aid Procurement and Transportation Decision-making in Governmental Agencies:
Bibliographic record
Abstract
Abstract This article conceptually and empirically examines sourcing of food aid, comparing the approaches promoted by the United States with those of the United Nations (UN) and the European Union (EU). In the recipient country approach (RCA) promoted by the United Nations and the European Union, transaction cost economics (TCE) suggests that RCA provides faster aid with fewer transaction costs. In the donor country approach (DCA) practiced by the United States, the resource-based view (RBV) suggests that the superior resources of a donor country assure a higher quality, safer, and plentiful food supply. Using a comparative case analysis with data provided by the United States Agency for International Development (USAID), we provide evidence that RCA and DCA as practiced in reality are both suboptimal. Improved sourcing and transportation options computed through quantitative methods can offer significant benefits over both approaches. We propose a contingency approach that reduces landed costs of food aid by giving governmental relief organizations more flexibility in RCA versus DCA sourcing, which can be justified by resource dependency theory (RDT). Our findings contribute to the decision-making and policy discussion about the efficiency of governmental food-aid programs.
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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 | 0.012 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".