Crime profiles and conditional release performance of psychopathic and non‐psychopathic sexual offenders
Bibliographic record
Abstract
Purpose. This study investigated the influence of psychopathy and sex offender subtype on criminal history, probability of being granted conditional release, and performance while on conditional release in a diverse group of violent offenders. We predicted that psychopathic sexual offenders would be associated with relatively prolific violent and sexual offending, a high probability of successful conditional release applications despite their past behaviour (resulting from ‘putting on a good show’ in a parole hearing), and poor performance in the community. Methods. Information was gathered via a correctional file review of 310 Canadian male federal offenders. Offenders were categorized into groups based on their sexual offence history (non‐sex offender, rapist, child molester, or mixed rapist/molester) and Psychopathy Checklist – Revised (PCL‐R; Hare, 2003) ratings. Their offences (sexual, violent, or non‐violent) and their complete conditional release histories were coded. Results. Psychopathy was associated with more violent and non‐violent, but not sexual, offences. A significant interaction between psychopathy and offender subtype revealed that psychopathy was associated with a greater number of sex offences within child molesters. High‐psychopathy offenders (both sexual and non‐sexual offenders) were about 2.5 times more likely to be granted conditional release than non‐psychopathic offenders. Conclusions. Psychopathy is associated with more prolific sexual offending among child molesters and – despite their extensive criminal histories and high recidivism rate – a great proficiency in persuading parole boards to release them into the community. Specialized education and training in dealing with psychopathic offenders is urgently needed.
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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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".