Bibliographic record
Abstract
Abstract The Oxford Handbook on Atrocity Crimes consolidates and further develops the evolving field of atrocity studies by combining major mono-, inter-, and multidisciplinary research on atrocity crimes in one volume encompassing contributions of leading scholars. Atrocity crimes—war crimes, crimes against humanity, and genocide—are manifestations of large-scale and systematic criminality committed within specific political, ideological, and societal contexts. These crimes are typically committed by multiple actors against a large number of victims who suffer far-reaching consequences. Scholars studying mass atrocities are scattered not only across disciplines—such as international (criminal) law, international relations, criminology, political science, psychology, sociology, history, anthropology, and demography—but also across the topic-related fields, which are by definition multi- and interdisciplinary but are typically limited to a particular category or aspect of atrocity crimes. This Handbook brings together these strands of scholarship and interrogates atrocity crimes as an overarching category of criminality, while simultaneously recognizing and theorizing differences among the individual constitutive categories. The Handbook covers topics related to the etiology and causes of atrocities, the actors involved, the victims of atrocity crimes and related harms, the reactions to atrocity crimes, and in-depth case studies of understudied situations of war crimes, crimes against humanity, and genocide.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.059 | 0.015 |
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".