A Prospective Study on Self-Reported Psychopathy and Criminal Recidivism Among Incarcerated Male Juvenile Offenders
Bibliographic record
Abstract
The present study examines the utility of three self-report measures of psychopathic traits in predicting recidivism among a sample of incarcerated male juvenile offenders. Participants ( N = 214, M = 16.40 years, SD = 1.29 years) from seven Portuguese juvenile detention centers were followed and prospectively classified as recidivists versus non-recidivists. Area under the curve (AUC) analysis revealed that the Antisocial Process Screening Device–Self-Report (APSD-SR) presented the best performance in terms of predicting general recidivism, with the Youth Psychopathic Traits Inventory (YPI) and the Childhood and Adolescent Taxon Scale–Self-Report (CATS-SR) presenting much poorer results. However, logistic regression models controlling for past frequency of crimes and age of first incarceration found that none of these self-report measures significantly predicted 1- or 3-year recidivism, whether general or violent. Findings suggest there are limitations in terms of the incremental utility of self-report measures of psychopathic traits in predicting recidivism among juveniles.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".