L’élément politique des crimes contre l’humanité: État des lieux de la jurisprudence de la Cour pénale internationale
Bibliographic record
Abstract
Abstract To be characterized as a crime against humanity under Article 7 of the Rome Statute of the International Criminal Court (ICC), the acts listed must have been committed as part of a systematic or widespread attack in furtherance of a State or organizational policy. Both variants of the attack, that is to say its “systematic” or “generalized” nature are alternative requirements. However, some of the legal literature since the preparatory work to draft the Rome Statute of the International Criminal Court in 1998 considers that the requirement of a policy makes both variants cumulative, hence creating a conflict between Article 7(1) and Article 7(2) of the Rome Statute. The controversy over the content and the legal scope of the concept of policy is worsened by the absence of definitions of the notions of policy and systematic attack in the core legal texts of the ICC. What definition have Chambers of the ICC given to the notion of policy? What sources have Chambers relied on? Does ICC case law provide tools to avoid possible conflict between Article 7(1) and Article 7(2) of the Rome Statute? These are the issues this study attempts to examine.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.003 | 0.042 |
| Scholarly communication | 0.012 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".