Psychopathy and offence severity in sexually aggressive and violent youth
Bibliographic record
Abstract
BACKGROUND: A large proportion of violent crimes are committed by youths. Youths with psychopathic traits may have a higher risk for recidivism and violence. AIMS/HYPOTHESES: Our aim was to compare sexually aggressive with violent young men on offence severity and psychopathy. Three hypotheses were proposed: first, young men with previous offences would display a progressive increase in seriousness of offence during their criminal career; secondly, the sexually aggressive and violent young men would not differ in scores on the Hare Psychopathy Checklist: Youth Version (PCL:YV); but, thirdly, PCL:YV scores would be positively correlated with the severity of the index crime, as measured by the Cormier-Lang System for Quantifying Criminal History. METHODS: Information was collected from the files of 40 young men in conflict with the law, and the PCL:Youth Version (YV) rated from this by trained raters. RESULTS: The offences of these young men became more serious over time, but we found no association between PCL:YV scores and offence type or seriousness. CONCLUSIONS AND IMPLICATIONS: This exploratory research suggests the importance of understanding the progression in offending careers, but a limited role for the PCL:YV in doing so. Given the small sample size, however, and the limit on access to information about details of age, the findings need replication.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".