Psychopathy, ADHD, and Brain Dysfunction as Predictors of Lifetime Recidivism Among Sex Offenders
Bibliographic record
Abstract
This study examines the best predictor of lifetime recidivism among Hare's psychopathy scores (PCL-R), attention deficit hyperactivity disorder (ADHD) diagnosis, and brain dysfunction measures in a sample of 1,695 adult male sexual, violent, and nonviolent offenders. Results indicated that most variables were associated with significantly more frequent recidivism. The best predictor of overall recidivism was the PCL-R, but more specifically, it was its items on criminal history that were associated with recidivism. Sexual offense recidivism was predicted by the presence of learning disorders; however, all measures were poor predictors. General recidivism was primarily associated with past criminal history and secondarily with learning disorders and ADHD. Results suggest that ADHD and brain dysfunction with criminal history measures are the best predictors for addressing the problem of criminal recidivism.
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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 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".