Bibliographic record
Abstract
Abstract As part of the counterinsurgency initiatives in Afghanistan and Iraq, military forces have been making payments to civilians in cases of ‘inadvertent’ injury, death and/or damage to property. There are no legal norms governing civilian compensation in war. Rather, military payments are seen as a way to help ‘win’ the hearts and minds of the population. This article examines this turn to military payments, with a focus on US practices and the implications for our understanding of contemporary changes to warfare. I suggest that while monetary payments can alleviate short-term economic need, the lack of legal liability is problematic as it may help amplify the impunity of warring soldiers. The article begins with an overview of the bureaucratic ways in which monetary values are attributed to death and injury. It then turns to consider how military payments reinforce the notion of ‘collateral damage’ that is legitimized in international humanitarian law. Finally, I draw upon theories of the gift, and of the gift of war, to interrogate the affective register in which military payments are made, inserted as they are in narratives of sympathy and condolence that bind the giver and receiver in relations of indebtedness and dependence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.066 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| 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".