“A Planet Shaker”: Educational Impacts of USAID’s Dismantling
Bibliographic record
Abstract
In early 2025, President Trump froze and then gutted the U.S. Agency for International Development (USAID), slashing more than $50 billion of aid spending. The speed and scale of Trump’s cuts sent shockwaves around the world and destabilized a global order in which the United States wielded tremendous financial and symbolic power through its foreign aid. Although significant media attention has been paid to cuts’ devastating ramifications for global public health, little is known about the consequences of the funding cuts to international development education. In this article, we drew on 62 interviews with actors situated within both global and national organizations to consider how USAID closures have transformed education globally. Our findings indicate that development actors see in this moment both widespread damage to education sectors and the possibility for newly configured aid and educational relations.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.025 | 0.012 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.009 |
| Insufficient payload (model declined to judge) | 0.008 | 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".