Youth with psychopathy features are not a discrete class: a taxometric analysis
Bibliographic record
Abstract
BACKGROUND: Recently, researchers have sought to measure psychopathy-like features among youth in hopes of identifying children who may be progressing toward a particularly destructive form of adult pathology. However, it remains unclear whether psychopathy-like personality features among youth are best conceptualized as dimensional (distributed along a continuum) or taxonic (such that youth with psychopathic personality characteristics are qualitatively distinct from non-psychopathic youth). METHODS: This study applied taxometric analyses (MAMBAC, MAXEIG, and L-Mode) to scores from two primary measures of youth psychopathy features: the Psychopathy Checklist: Youth Version (N = 757) and the self-report Antisocial Process Screening Device (N = 489) among delinquent boys. RESULTS: All analyses supported a dimensional structure, indicating that psychopathy features among youth are best understood as existing along a continuum. CONCLUSIONS: Although youth clearly vary in the degree to which they manifest psychopathy-like personality traits, there is no natural, discrete class of young 'psychopaths.' This finding has implications for developmental theory, treatment, assessment strategies, research, and clinical/forensic practice.
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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 | 0.014 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.010 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".