Identifying the cutoff score for the PCL-R scale (psychopathy checklist-revised) in a Brazilian forensic population
Bibliographic record
Abstract
This study introduces a Portuguese-language version of psychopathy checklist-revised (PCL-R) [Harv. Mental Health Lett. 12 (1995) 4] in the Brazilian penitentiary system. Hare’s scale is used extensively in many other countries. In a forensic population sample of 56 male subjects classified as psychopaths and non-psychopaths [Manual for the Hare Psychopathy Checklist-Revised, Multi-Health System, Toronto, 1991], 33 correlated to global personality disorder (GPD) and 23 to partial personality disorder (PPD), respectively, subtypes of antisocial personality disorder [Manual for the Hare Psychopathy Checklist-Revised, Multi-Health System, Toronto, 1991]. Subjects were evaluated through psychiatric and neurological examinations, review of judicial records, Rorschach and PCL-R. A control group of 30 subjects without criminological or psychiatric history was also evaluated with the same instruments. PCL-R validation and identification of cutoff score for Psychopathy (GPD group) was assessed through the concurrent use of the Rorschach. PCL-R cutoff score for the Brazilian population was set at 23. Sensitivity was determined at 84.8%, and reliability was high (Kappa index 0.87). GPD individuals were characterized as clearly psychopathic according to PCL-R criteria while PPD individuals can only be considered mildly psychopathic, with better chance of rehabilitation.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".