Where are the Criminologists?: The Persistent Absence of the Field in Genocide Research
Bibliographic record
Abstract
The rallying cry of “Never Again” was heard worldwide after the Nazis’ annihilation of Jews and other victims were discovered after WWII. The global community promised that the crime of genocide would be prevented and signatory parties of the United Nations Convention on the Prevention and Punishment of Genocide (December 9, 1948) would do everything in their power to prevent another Holocaust from occurring. Unfortunately the “crime of all crimes” remains an almost permanent fixture in international news while signatory parties remain reluctant to call the ongoing conditions genocide. The field of genocide studies is built upon an interdisciplinary field of scholars but notably absent from the vast majority of that scholarship are criminologists, who are uniquely trained to reduce and prevent crimes of all types. This study aims to explore seven peer-reviewed genocide journals to determine the extent to which criminology and/or criminologists are present. Our findings demonstrate a continued lack of theoretical contributions and applications from the field of criminology/criminal justice to the study of 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 | 0.012 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.016 | 0.023 |
| Scholarly communication | 0.017 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".