Comparing the Youth Psychopathic Traits Inventory (YPI) and the Psychopathy Checklist–Youth Version (PCL-YV) Among Offending Girls
Bibliographic record
Abstract
Using a multimeasure longitudinal research design, we measured psychopathy with the Youth Psychopathic Traits Inventory (YPI) and the Psychopathy Checklist-Youth Version (PCL-YV) among 122 offending girls. We examined the psychometric properties of the YPI, investigated the association between the YPI and the PCL-YV, and assessed their concurrent and longitudinal association with externalizing problems on the Youth/Adult Self-Report and violent and delinquent behaviors on the Self-Report of Offending. Alphas for the YPI were adequate and there were small to moderate correlations between the YPI and PCL-YV, suggesting that each assesses distinctive personality features. The YPI and the PCL-YV were approximately equivalent in their association with concurrent and longitudinal outcomes with two exceptions, where the YPI demonstrated a stronger association with antisocial behavior. Concurrently, there was a divergent relationship between the psychopathy factor scores and antisocial outcomes. Within 2 years, the psychopathy affective factor, which constrained the YPI and PCL-YV to be equivalent, was associated with externalizing behaviors and the YPI affective factor was associated with violent offending. Approximately 4½ years later, neither measure was significantly related to antisocial behavior after accounting for past behavior. Reasons for continuity and discontinuity in risk identification 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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".