Transnational Crimes, Terrorism and Torture
Bibliographic record
Abstract
Introduction Overview To focus only on the ‘core crimes’ and their prosecution would be to ignore a substantial area of criminal law with international implications; there are other crimes of international concern which have a huge impact on people throughout the world and on global economic development. Crimes which are the subject of international suppression Conventions but for which there is as yet no international criminal jurisdiction, are the focus of this chapter. They are here termed transnational crimes. These are crimes which have actual or potential transboundary effect and crimes which are intra-State but which offend a fundamental value of the international community. The prevention and punishment of transnational crimes requires cooperation among governments and among law enforcement agencies. A growing number of agreements are being concluded to provide for this in relation to such crimes as drugs trafficking, piracy, slavery, terrorism offences, torture, apartheid, enforced disappearances, transnational organized crime including people trafficking, smuggling migrants and illegal arms trafficking, and corruption. Some of these crimes are also crimes of customary international law or are international crimes when committed in certain circumstances (for example as crimes against humanity). They include those which were listed as ‘treaty crimes’ in the ILC draft of the ICC Statute, but which were excluded from the Rome Statute in the course of the negotiations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".