Apprehension of Indicted War Criminals: Lessons from the Former Yugoslavia
Bibliographic record
Abstract
The tasks entrusted to peace support operations (PSOs) have increased in complexity and risk since the end of the Cold War. One of the most important and challenging of these new duties is securing the arrest, detention and transfer of indicted war criminals during times of armed conflict and occupation. Establishing an effective legal framework to govern this activity presents distinct challenges, and important lessons may be learned from the diverse efforts made to bring indicted war criminals to justice in the wake of the armed conflict in the former Yugoslavia. This chapter begins by identifying key aspects of this international law enforcement activity that makes it unique from the domestic context. The arrest, detention, surrender and transfer regime developed at the International Criminal Tribunal for the former Yugoslavia (ICTY) is then set out, and leading judicial decisions are considered. Finally, controversial means that have been used to apprehend indicted war criminals, including deceptive tactics and transnational abduction, will be critically examined. In the background of this discussion are two fundamental questions: (1) How far should peace support operations go to apprehend a person charged with war crimes? (2) How do we reconcile the international community’s interest in bringing indicted war criminals to justice with the rights of the accused and state sovereignty?
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".