Psychopathic traits in adolescent offenders: an evaluation of criminal history, clinical, and psychosocial correlates
Bibliographic record
Abstract
Although a large body of research has established the relevance of psychopathy to adult offenders, its relevance to adolescent offenders is far less clear. The current study evaluated the clinical, psychosocial and criminal correlates of psychopathic traits in a sample of 226 male and female incarcerated adolescent offenders. According to an 18-item version of the Psychopathy Checklist-Youth Version (PCL-YV; Forth, Kosson, & Hare, 2003), only 9.4% exhibited a high level of psychopathic traits (PCL-YV>/=25). Consistent with past research, higher PCL-YV scores were positively associated with self-reported delinquency and aggressive behavior and were unrelated to emotional difficulties. Although higher PCL-YV scores were associated with the experience of physical abuse, the only psychosocial factor to predict PCL-YV scores was a history of non-parental living arrangements (e.g. foster care). In terms of criminality, a violent/versatile criminal history was positively associated with psychopathic traits. However, PCL-YV scores were unrelated to participants' official criminal records for total, non-violent, violent, and technical violation convictions. In conclusion, the data partially support the construct validity of psychopathy with adolescent offenders, but some inconsistencies with prior adult and adolescent psychopathy research were evident. These issues are discussed.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".