Incorporating Strengths Into Quantitative Assessments of Criminal Risk for Adult Offenders
Bibliographic record
Abstract
The primary aim of this study is to determine the extent to which the consideration of strengths enhances the predictive validity of risk assessment protocols applied to correctional populations. Data from the Service Planning Instrument (SPIn) Pre-Screen were analyzed for 3,656 adult offenders bound by provincial supervision across Alberta, Canada. The predictive validity of the screening instrument was equivalent across gender and Aboriginal status (areas under the curve [AUCs] = .75-.77). Hierarchical logistic regression revealed significant main effects for risk and strength subtotals in predicting new offenses over 18 months for the overall sample, indicating that the inclusion of strengths adds uniquely to the prediction of recidivism. The overall model yielded a significant Risk Score × Strength Score interaction, illustrating that high strength scores are particularly effective in attenuating recidivism among higher risk cases. Rather than limit their consideration to case management contexts, results support the integration of strengths into quantitative assessments of criminal risk.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".