Change in Level of Service Inventory–Ontario Revised (LSI-OR) Risk Scores Over Time: An Examination of Overall Growth Curves and Subscale-Dependent Growth Curves
Bibliographic record
Abstract
The dynamic nature of risk to re-offend is an important issue in the management of offenders and has stimulated extensive research into dynamic risk factors that can alter an individual's overall risk to re-offend if addressed. However, few studies have examined the relative importance of these dynamic risk factors, complicating the task of developing case management and treatment plans that will effect the most change. Using a large, high-risk sample and multi-wave data of a common risk assessment tool, the Level of Service Inventory-Ontario Revised (LSI-OR), the current study investigated the relationship among criminogenic risk factors and their role in influencing the overall risk score. Results indicated a diverse pattern of effects on the eight subscale scores, specifically suggesting that changes on Procriminal Attitude/Orientation, Criminal History, and Leisure/Recreation subscales resulted in a quicker rate of change to the overall risk score over time. These results suggest that some factors may be driving the change in overall risk and could potentially effect the most change if prioritized for intervention. Practical implications and implications for further research 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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".