Foreign Aid Allocation from a Network Perspective: The Effect of Global Ties
Bibliographic record
Abstract
This article examines competing explanations for foreign aid allocation on the global level and argues for a new approach to understanding aid from an institutionalist perspective. Using network data on all official bilateral aid relationships between countries in the period from 1975 through 2006 and data on recipient country ties to world society, the article offers an alternative explanation for the allocation of global foreign aid. Fixed effects negative binomial regression models on a panel sample of 117 developing countries reveal that global ties to world society in the form of non-governmental memberships and treaty ratifications are strong determinants of the network centrality of recipient countries in the global foreign aid network. Countries with a higher level of adherence and connection to world society norms and organizations are shown to be the beneficiaries of an increased number of aid relationships with wealthy donor countries. The findings also suggest that prior explanations of aid allocation grounded in altruist or realist motivations are insufficient to account for the patterns of aid allocation seen globally in recent years.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".