The Draft Convention on Crimes Against Humanity: What to Do with the Definition?
Bibliographic record
Abstract
A centrally important and influential feature of the Draft Convention on Crimes Against Humanity will, obviously, be its definition of the crime. It is most likely that the Draft Convention will use the definition from Article 7 of the Rome Statute. There are however significant legitimate concerns about aspects of Article 7, most particularly the “policy element”. This chapter proposes that commentary to the Draft Convention can mitigate the concerns by highlighting key points from pertinent authorities. The proposed commentary draws on national jurisprudence and other authorities, as well as the logical structure of Article 7, showing that the policy element is simply an in limine filter screening out situations of unconnected ordinary crimes. The current debate has neglected valuable national judicial contributions that help harmonize the seemingly fractured international sources, and they do so in a way that promotes a workable definition.
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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 | 0.021 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.029 |
| Scholarly communication | 0.016 | 0.010 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.016 | 0.019 |
| Insufficient payload (model declined to judge) | 0.003 | 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".