Bibliographic record
Abstract
Introduction Overview Genocide, as General Assembly Resolution 96(1) declared, ‘is a denial of the right of existence of entire human groups, as homicide is the denial of the right to live of individual human beings’. It is a crime simultaneously directed against individual victims, the group to which they belong, and human diversity. The legal concept of genocide is narrowly circumscribed, the term ‘genocide’ being reserved in law for a particular subset of atrocities which are committed with the intent to destroy groups, even if colloquially the word is used for any large-scale killings. Most of the crimes committed by the Pol Pot regime in Cambodia in 1975–78, for example, are atrocities which do not readily fit within the narrow definition, however dreadful the suffering they caused. A decision that a particular atrocity is not ‘genocide’ does not of course remove the moral or legal guilt for conduct that falls within the definition of other international crimes. Many acts which do not constitute genocide will constitute crimes against humanity. The form of intent that is a necessary element of the crime, that of intending to destroy a group, marks it out from all other international crimes. This explains why genocide is regarded as having a particular seriousness, and has been referred to as the ‘crime of crimes’.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.016 |
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".