Forensic Assessment With the PAI in Correctional Samples: Implications for RNR
Bibliographic record
Abstract
The present investigation examined Risk-Need-Responsivity (RNR) correlates of Personality Assessment Inventory (PAI) scores in a sample ( N = 377) of offenders with diverse criminal histories. It was hypothesized that PAI scales with content reflective of criminogenic needs would be associated with recidivism while those indicative of major mental illness and behavioral disruption would be positively linked to responsivity variables. Several PAI scales predicted general and violent recidivism, particularly those reflective of criminogenic need. More serious profile patterns were associated with younger age, less education, lower cognitive ability, and sexual offense treatment attrition, per the responsivity principle. Finally, an exploratory factor analysis identified four PAI factors: Major Mental Illness, Extraversion, Paranoia, and Antisociality. Antisociality scores were the most predictive of general and violent recidivism. Antisociality and Major Mental Illness scores also predicted treatment attrition. Study findings suggest that the PAI can be a useful adjunct to standardized risk and need measures for RNR-informed assessment.
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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 | 0.009 | 0.037 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".