Bibliographic record
Abstract
In US counterinsurgency doctrine, money has been characterized as “ammunition” and as a “weapons system”. Money is being wielded to win over the “hearts and minds” of the population, and to protect the lives of the occupying forces. Soldiers are taking on greater responsibility for spending money on reconstruction and development projects on the battlefield. Billions of dollars have been spent by the military in Iraq and Afghanistan on a wide range of projects including building schools, developing infrastructure, and providing agricultural assistance as well as microfinance. But military doctrine now extends to helping implement free-market economies, supporting business creation, setting up banking facilities, and promoting entrepreneurialism. In fact, economic development has been recast as a constitutive form of combat, not simply as a supplement to conventional warfare, or as part of post-conflict reconstruction. The use of money as a “weapons system” speaks to both a different kind of military and a different kind of war. Fighting and violence have not been replaced or even displaced, but are joined with new strategies and tactics that sit uneasily side by side. As soldiers have been retooled to be economic decision-makers, we need to better understand how money and markets are increasingly both the weapon of military intervention and the anticipated outcome.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.034 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".