Bibliographic record
Abstract
Abstract Scholars from different disciplinary backgrounds have studied why perpetrators of international crimes commit these horrendous acts. Initially, historians and psychologists focused on this debate, which was heavily centred on the Second World War. Over the years, scholars with more diverse disciplinary backgrounds, studying a wide array of cases, using both qualitative and quantitative research methods, began to investigate perpetrators of international crimes and terrorism. Recently, this multi- and interdisciplinary debate has become known as perpetrator studies. This is the first book to take stock of the state of the art of this new field of study. It analyses the most prominent theories, methods, and evidence to determine what we know and what we think we know about perpetrators, as well as the ethical implications of gathering this knowledge. It traces the development of perpetrator studies while pushing the boundaries of the field by including contributions from authors from a wide array of disciplines, including criminology, history, law, sociology, psychology, political science, religious studies, and anthropology. Authors cover numerous case studies, including prominent ones such as Nazi Germany, Rwanda, and the former Yugoslavia, but also those that are relatively under-researched and more recent, such as Sri Lanka and the Islamic State, and use various research methods, including but not limited to, trial observations and interviews.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 0.004 |
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".