Capitalizing on Conflict: How U.S. arm sales fuel the humanitarian crisis in Yemen
Bibliographic record
Abstract
U.S. weapons manufacturers fueling the crisis in Yemen spend big money on lobbying but make even more selling arms.Over the last 20 years, defense companies and their affiliates have spent more than $2.6 billion on lobbying politicians and $300 million making contributions to support and influence their campaigns. Getting up to half of a Pentagon budget that is likely to top $800 billion next year makes it well worth the effort. U.S. manufacturers make billions from federal government contracts supplying weapons to the world's most expensive and well-armed military, and billions more selling arms abroad. Over the last five years, the U.S. accounted for 39% of global arms exports according to Stockholm International Peace Research Institute. Of those exports, 43% went to the Middle East. The largest recipient, Saudi Arabia, received nearly a quarter of U.S. exports. Both Saudi Arabia and the United Arab Emirates (UAE) are among the top 10 recipients, putting them on par with stalwart allies like Australia, the United Kingdom and Japan.For years, the Saudi-led coalition has used those weapons in a catastrophic civil war in Yemen that is now in its eighth year. The resulting humanitarian crisis has claimed over a hundred thousand lives from military conflict, famine and disease.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".