The Predictive Validity of the LS/CMI with Aboriginal Offenders in Canada
Bibliographic record
Abstract
This study examined the applicability of a general risk/need assessment tool, the Level of Service/Case Management Inventory (LS/CMI), to a large sample of Aboriginal offenders ( n = 1,692) and compared the predictive validity with that of the rest of the cohort, a sample of non-Aboriginal offenders ( n = 24,758). It examined the use of the clinical override with offenders. Aboriginal offenders had considerably higher scores and a greater recidivism rate than non-Aboriginal offenders. Internal consistency was high and virtually identical for both samples. The predictive validity for Aboriginal offenders on general recidivism was high, although slightly higher for non-Aboriginal offenders. The predictive validity was significant but low on violent recidivism for Aboriginal offenders, as were numerous subscales. Assessors used the override feature to change risk level less frequently on Aboriginal offenders. The implications of this study for policy (use on ethnic minority offenders) and practice (how to interpret potential recidivism) are discussed.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".