Surrendering The Higher Ground: The Abuse Of Combatants During War
Bibliographic record
Abstract
What explains the differences in the ways captor states choose to treat enemy prisoners during war? Why are the rights of some prisoners rigorously upheld, while in other cases they are viewed as expendable? I argue the abuse of captured combatants largely flows from the nature of the belligerents and bloodshed during war. According to the nature of the belligerents, I argue that democracies are less likely on average to abuse prisoners than their autocratic counterparts. However, I propose the reasons for this democratic distinctiveness are less a function of domestic norms of individual rights or nonviolence, but more due to the greater sensitivity of democracies to fears over retaliation as well as to the strategic benefits possible through good conduct. On the other hand, the bloodshed wrought by the aims states seek to attain through war, along with the severity of the fighting, strongly increase the chances that states will abuse their prisoners. In order to assess these arguments, I construct a new data set on the treatment of captured combatants during all interstate wars from 1898 to 2003. I then provide further support for the findings from the quantitative analysis using a series of in-depth case studies of several episodes of both the abuse and humane treatment of enemy prisoners.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".