The PCL: YV and re-offending across ethnic groups
Bibliographic record
Abstract
Purpose – The psychopathy checklist: youth version (PCL: YV) checklist is an assessment of youth psychopathic traits and is regularly validated by way of its associations with re-offending and violence. Yet existing research has been conducted with predominantly white Caucasian cohorts and extant evidence suggests that associations with recidivism are stronger in samples with greater proportions of white offenders. The paper aims to discuss these issues. Design/methodology/approach – This study investigated the cross-cultural validity of the PCL: YV for an ethnically diverse Australian sample of 175 young male offenders in custody. Participants were assessed in custody with the PCL: YV and offending data were collected post-release for up to 18 months. Findings – PCL: YV total and domain scores were comparable across ethnicity; however the instrument demonstrated stronger relationships with recidivism for Australian participants with an English speaking background compared to Indigenous and culturally and linguistically diverse participants. Practical implications – The authors advocate the cautionary employment of the PCL: YV as a violence risk prediction instrument with minority young offenders regionally, pending further evidence. Originality/value – This study addresses the capacity of the PCL: YV to predict violence across different ethnic groups. Cross-cultural youth psychopathy research is currently inadequate and existing studies suggest that the PCL: YV is a weaker predictor of violence in culturally diverse samples. This investigation provides much needed information on the capacity of the PCL: YV to extend to different ethnic groups who are represented Australia’s youth prison population. This is the first study of its kind regionally, and more importantly is the first PCL: YV study with an Indigenous Australian comparison group. This is particularly important given that Indigenous Australians are heavily overrepresented in Australia’s criminal justice system and require appropriate risk assessment measures to ensure they are not misclassified. Research such as this is now of particular interest given the recent judicial decision made in Ewert vs Canada.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".