The Level of Service Inventory (Ontario Revision) scale validation for gender and ethnicity : addressing reliability and predictive validity
Bibliographic record
Abstract
Previous investigations of the Level of Service Inventory – Ontario Revision (LSI-OR) have examined individual subgroups of offenders (e.g., women, Aboriginal offenders), which has made comparisons of its predictive validity between specific offender groups suspect. This study was conducted on a complete cohort of 26,450 offenders who were released from Ontario provincial correctional facilities, sentenced to a conditional sentence, or who began a term of probation in 2004. Participants were followed up for at least four years to collect recidivism information on numerous subgroups of offenders including males (81.7%), females (18.3%), Aboriginal (6.4%), Black (7.3%) and Caucasian offenders (59.2%). Analyses revealed that the LSI-OR scores are positively correlated with recidivism (r = .441, p < .001), and similar correlations were found for all offenders regardless of gender or race, (Aboriginal r = .377, p < .001; Black, r = .420, p < .001; Caucasian, r = .417, p < .001; Male, r = .439, p < .001; Female, r = .426, p < .001). LSI-OR scores are also correlated with severity of the recidivism offence, (r = .098, p
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".