SAME GENUS, DIFFERENT INTENT: DISTINGUISHING PERSECUTION AS A CRIME AGAINST HUMANITY FROM GENOCIDE
Bibliographic record
Abstract
The crime against humanity of persecution and the crime of genocide share common roots which go way back to Nuremberg, although both have developed differently. The concept of genocide has remained untouched since the adoption of the Genocide Convention in 1948. The same does not hold true for persecution, whose definition and scope under the Rome Statute is reflective of the historical difficulty in identifying this offence as a stand-alone crime. The early stages of both offences represent two circles that intersect, but the specific intent to destroy a victimised group is what makes genocide to fall into an exclusive and convoluted category of crimes. This article aims at evaluating persecution and genocide from a substantive and technical perspective, with a view to examining the conditions under which the two crimes operate. The assessment may help clarify some critical points concerning the applicability of both offences in the context of crimes committed in a systematic and targeted fashion.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.054 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".