“Train the World”: Examining the Logics of US Foreign Military Training
Bibliographic record
Abstract
Abstracts Foreign military training has become a key component of the United States’ security policy. What explains the variation in US training allocation across countries and over time? Past work on security assistance, such as training, focuses on its effectiveness and consequences, largely overlooking questions about which countries receive it in the first place. To understand what drives US military training partnerships, we conducted a global statistical analysis of training from 1999 to 2018, structured around four logics: building relationships through defense diplomacy, deterrence against external, interstate threats, capacity-building in fragile states, and promoting democratic norms to advance democracy around the world. We find that the four logics receive support, with relationship-building and response to interstate and internal threats most consistently so. This analysis demonstrates the different ways the United States has used training in support of the US-led global order and raises questions about how to achieve accountability given these multiple logics. More broadly, the findings also have relevance for understanding how other states allocate training in conjunction with, in emulation of, or in opposition to the United States.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 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".