Between caution and controversy: lessons from the Gulf Arab states as (re-)emerging donors
Bibliographic record
Abstract
The history of Gulf donorship, its trajectory and underlying motivations, continues to be an understudied aspect of foreign aid. While the Gulf Arab states are not new donors, their manner of regional coordination, branding, and aid management are distinct. Often helping fellow countries of the South, particularly Arab and Muslim countries, these countries have moved towards stronger private sector involvement and into social spending programmes. Owing to their oil wealth, Gulf Arab states' are increasingly generous and yet they are also cautious after 9/11 about how and by whom their aid is channelled. Nevertheless, with oscillations in oil prices, continued controversy over rising Islamism post-Arab-Spring, the future of Gulf aid remains a valuable subject of study.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.021 | 0.023 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.010 | 0.012 |
| Insufficient payload (model declined to judge) | 0.009 | 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".