LIMITING THE RISK TO COMBATANT LIVES: CONFLUENCES BETWEEN INTERNATIONAL HUMANITARIAN LAW AND BUDDHISM
Bibliographic record
Abstract
This article places international humanitarian law (IHL) side by side with Buddhist narratives as seen through the Jātakas, to investigate how they view the expectation placed on soldiers to risk their lives in battle. To this end, I delve into the notion of reciprocity of risk in battle from an IHL perspective, which I argue is crucial to infusing warfare with restraint. Similarly, Buddhism acknowledges the importance of reciprocity as an ethical principle that leads to non-violence. I demonstrate how IHL tries to ensure that the risk combatants undertake in combat is limited through its rule of surrender. I compare this argument with the Seyyaṃsa or Seyya Jātaka (no. 282), which illustrates the need to cease violence in cases of surrender. The way militaries treat their own combatants is crucial to the meaningful practice of surrender and thereby the limits and restraints of warfare. Buddhism too encourages rulers to value the lives of their soldiers and not to put their lives at unnecessary risk. I conclude that to maximise the combatant’s choice to limit the risk he takes in battle, IHL should pay more attention to the orders that militaries and armed groups issue to their combatants. Buddhism, for its part, can facilitate the constructive use of military orders because it projects positive images of rulers who are reluctant to order their soldiers to take unnecessary risks in war.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.081 |
| Scholarly communication | 0.014 | 0.008 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.009 |
| 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".