Tracking the rise of United States foreign military training: IMTAD-USA, a new dataset and research agenda
Bibliographic record
Abstract
Training other countries' armed forces is a go-to foreign policy tool for the United States and other states. A growing literature explores the effects of military training, but researchers lack detailed data on training activities. To assess the origins and consequences of military training, as well as changing patterns over time, this project provides a new, global dataset of US foreign military training. This article describes the scope of the data along with the variables collected, coding procedures, and spatial and temporal patterns. We demonstrate the added value of the data in their much greater coverage of training activities, showing differences from both existing datasets and aggregate foreign military aid data. Reanalyzing prior research findings linking US foreign military training to the risk of coups d'état in recipient states, we find that this effect is limited to a single US program representing a small fraction of overall US training activities. The data show comprehensively how the United States attempts to influence partner military forces in a wide variety of ways and suggest new avenues of research.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".