Nabbing the devil : practical considerations in the use of armed force in the apprehension and arrest of persons indicted for war crimes
Bibliographic record
Abstract
This thesis considers the challenges faced by international criminal tribunals in gaining physical jurisdiction over those persons indicted for the commission of war crimes, crimes against humanity and genocide. The thesis covers the need for justice for victims of such crimes, the history of the laws of war, war crimes and their prosecution, the need for an interdiction instrument, the legal basis for acting with force to arrest indictees, the use of military force to effect such arrests, and some of the various political and practical issues that arise in such use of force. I sought out first hand quotes and stories contained in various media sources, books and court transcripts to lend a voice to the victims. Substantiating the requirement for justice, I researched the written works and oral texts of academics, politicians, jurists, and senior military commanders, who have experienced firsthand the difficulties in preventing atrocities and prosecuting accused. To concisely discuss the history of the laws of war, I studied various academic works on the conduct of war including the writings of various history, religious and legal academics, as well as several primary source documents, including religious texts. In considering current international tribunals, I relied on treaty and customary international law documents, United Nations' documentation, and the current tribunals' statutes. The case law on extraterritorial detention of accused was found in trial and appellate court decisions from the United States, United Kingdom, South Africa, Israel and the ICTY. The thesis concludes that current international tribunals lack necessary mechanisms for enforcing indictments and thus ensuring that accused are brought before the courts' jurisdiction. In light of this inadequacy, a practical mechanism is needed to effect the interdiction and arrest of indictees for current and future international criminal tribunals. In conclusion, the use of military force to secure the detention and delivery of accused before the jurisdiction of issuing courts can be justified and should be utilized when other options have failed to effect with celerity, the accused's arrest.
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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 | 0.009 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.020 | 0.028 |
| Scholarly communication | 0.014 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".