The United Arab Emirates as a global donor: what a decade of foreign aid data transparency reveals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The United Arab Emirates (UAE) has become a leading contributor of foreign aid, in terms of percentage of gross national income as well as in total amount. Historically, Emirati aid was opaque, and little was known about the foreign aid portfolio. This changed after 2009 when the UAE began to submit detailed, project-level data to the Development Assistance Committee of the OECD. Based on a decade of aid transparency, this article carries out an examination of the political economy of aid provided by the UAE, comparing its portfolio to other donor countries. Particular attention is paid to analyzing three primary recipients of its aid (Egypt, Serbia and Yemen) and the implicit motivations driving those decisions. The majority of Emirati aid to these three countries was granted as general budgetary support, often in tandem with efforts to achieve political, economic and/or military aims. Based on the findings, an evaluation is made regarding Emirati narratives of South-South cooperation and its seeking of mutual benefit as well as critiques put forward within the literature countering this. In addition to critically assessing the details of an under-researched aid portfolio, this paper highlights areas for further study to deepen our understanding of the UAE’s foreign aid.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it