Antisociality and the Construct of Psychopathy: Data From Across the Globe
Bibliographic record
Abstract
Previous theory and research on the structural, longitudinal, and genetic nature of psychopathy have provided strong conceptual and empirical evidence that overt antisociality is a component of the psychopathy construct (Hare & Neumann, 2008, 2010; Lynam & Miller, 2012). However, determination of the strength of the association between antisociality and other psychopathic features has not been explored systematically. The current article draws on previously published large North American studies, as well as data from across the globe, to estimate the strength and pattern of the associations between overt antisociality and other psychopathic domains in a diverse set of samples. Structural equation modeling was used to estimate model parameters from samples that had data on either the Psychopathy Checklist-Instruments (PCL-R, PCL: YV, PCL: SV) or self-report assessments that have known latent structures (SRP, B-Scan 360). In addition, two relatively large samples (male offenders and young adult males), assessed with both the PCL-R and the SRP, provided an opportunity to examine the link between antisociality and the other psychopathy domains across different assessment methods. The overall findings indicate that the associations were moderate to strong, depending on the nature of the sample, and clearly indicate that antisociality is a core component of the psychopathy construct.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".