Variants of psychopathic personality in Korean and UK incarcerated offenders: Using latent profile analysis and discriminant analysis
Bibliographic record
Abstract
Since the popularization of the Psychopathy Checklist-Revised (PCL-R), research on the construct of psychopathy has drastically increased. However, there has been little research examining the extent to which psychopathy varies across different cultures. This study is the first to use latent profile analysis to examine cultural variations in psychopathic traits between large samples of male inmates in Korean (n = 1102) and UK (n = 1316) prisons. Supplementary discriminate analysis was also used to validate the classification profiles and determine which items of the PCL-R were most important in defining the differences or similarities between each of the classes in the two large samples. Based on the analysis, two variants of primary psychopathy could be distinguished in the Korean sample but not in the UK sample. Conversely, only secondary psychopathy was identified in the UK sample. This result was also confirmed by supplementary analysis, which verified classification accuracy and also provided structure matrixes listing the correlations between each PCL-R item and the two discriminant functions. Our results point to the possibility of cultural differences in the structure of psychopathy and provide practical implications for clinical assessment and diagnosis.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".