Predicting Institutional Violence: Utility of the Psychopathy Checklist-Revised in a Mexican Prison Context
Bibliographic record
Abstract
Given the high prevalence of institutional violence within the Mexican prison system, the need for validated risk assessment measures is urgent. However, research on the predictive validity of such tools has been limited mainly to White, Educated, Industrialized, Rich, and Democratic samples. This prospective study used quantitative methods to examine the effectiveness of the Psychopathy Checklist-Revised (PCL-R) in predicting institutional violence in a sample of incarcerated individuals in Mexico over 3 months. Data were collected through semi-structured interviews and prison record reviews from 114 adult males in a medium-security prison in Mexico City. Results showed that the PCL-R total score, Factor 2, and Facets 1, 3 and 4 were significant predictors of institutional violence. These findings have practical implications for risk assessment and management within Mexican correctional populations. Recommendations are offered to enhance the methodological rigor of future research endeavors in this area.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| 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".