Improving Civilian Protection during War through Conflict-Specific Behavioural Regulation of Combatants
Bibliographic record
Abstract
This thesis advances the claim that there is a gap between the regulation of behaviour for the protection of individuals in peace and the regulations needed to protect civilians from combatant violence during war. Social psychology and criminology theories can help to develop the necessary conflict-specific behavioural regulations. This is because social psychology and criminology theories can explain how combatant deviance is adversely affected by psychological processes that reframe combatants’ conceptions of right and wrong and, in so doing, fundamentally alter the way in which combatants view the IHL rules intended to protect civilians. This thesis uses legal doctrinal methodology to establish the current status of IHL application to armed groups and existing IHL protections for civilians, which are based largely on peacetime protections for individuals (e.g., prohibitions on assault, murder, rape, etc.). It demonstrates the need and utility of turning to academic disciplines beyond law, specifically social psychology and criminology, to understand combatant violence toward civilians. Through the use of case studies focusing on the Sierra Leone civil war and the numerous ongoing conflicts in the Democratic Republic of Congo, this thesis identifies two common combatant behaviours that contribute to the perpetration of IHL violations against civilians, but are currently unregulated by IHL: (1) combatant use of demeaning, degrading, or dehumanizing language toward civilians and (2) combatant use of nicknames, particularly violent or heroic nicknames. The thesis proposes two new IHL regulations to address these behaviours and to inhibit the ability of these behaviours to contribute to violence toward civilians during armed conflict. Ultimately, the thesis demonstrates how combatant psychology can be used to develop the substantive content of IHL for the protection of civilians.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".