Using latent variable- and person-centered approaches to examine the role of psychopathic traits in sex offenders.
Bibliographic record
Abstract
The current study employed both latent variable- and person-centered approaches to examine psychopathic traits in a large sample of sex offenders (N = 958). The offenders, who had committed a range of sexual crimes, had been assessed with the Psychopathy Checklist-Revised (PCL-R; Hare, 2003). Structural equation modeling results indicated that the four-factor model of psychopathy (Hare, 2003; Neumann, Hare, & Newman, 2007) provided good representation of the dimensional nature of psychopathic traits across the sample of offenders, and that the PCL-R factors significantly predicted sexual crimes. In particular, the Affective and Antisocial psychopathy factors each predicted sexually violent crimes. Latent profile analysis results revealed evidence for a 4-class solution, with the subtypes showing distinct PCL-R facet profiles, consistent with previous research. The four subtypes were validated using sexual crime profiles. The prototypic psychopathy subtype (high on all 4 PCL-R facets) evidenced more violent sexual offenses than did the other subtypes. Taken together, the results demonstrate how variable- and person-centered approaches in combination can add to our understanding of the psychopathy construct and its correlates. (PsycINFO Database Record
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".