The Role of Intellectual Functioning in the Prediction of Release Outcome and Recidivism in Federally Sentenced Offenders
Bibliographic record
Abstract
This retrospective, archival study investigated the role of intellectual functioning in two correctional outcomes; release outcome and recidivism. It also examined the use of a psychological assessment battery, made up of various self-report and performance measures, in the prediction of the outcomes. The battery included psychometric measures of intelligence, personality, test-taking response style, and psychological distress. Standardized risk assessment tools were used to control for actuarial risk. The sample (N = 168) were federally sentenced, adult male offenders on conditional release in the Ontario region. Based on a series of logistic regressions, primary findings included a significant, inverse relationship between intelligence and both release outcome and recidivism, with lower IQ scores predicting increased likelihood of revocation and reoffending (odds ratios 4% and 3%, respectively). IQ accurately classified successful and unsuccessful groups in both outcomes at a rate on par with or approaching the classification accuracy of the actuarial risk assessment tools studied. The psychological assessment battery predicted release outcome with less accuracy, as only intelligence and personality measures were found to be significant predictors, and the battery was not predictive of recidivism. When actuarial risk was controlled, IQ only approached significance as a predictor in the release outcome, and was not a significant predictor in the recidivism outcome. However, the significance of IQ as a predictor varied according to which risk assessment tool was used to control for measured risk. Survival analysis revealed that intelligence played a role in time to revocation and reoffending, as lower IQ scores were associated with faster rates of revocation and recidivism. From a clinical perspective, this study demonstrates the importance of assessing intelligence in offender populations to inform risk management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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".