“A Planet Shaker”: Educational Impacts of USAID’s Dismantling
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
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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