An Examination of the Professional Override of the Level of Service Inventory–Ontario Revision
Bibliographic record
Abstract
This study examined the nature and impacts of the professional override on the Level of Service Inventory–Ontario Revision (LSI-OR), using a large archival database of 40,539 individuals’ information. Research questions focused on the predictive validity of various LSI-OR risk metrics, including total risk/need scores, initial risk categories, and adjusted risk categories, for various types of recidivism; how professional overrides were used; whether they were used more with some groups than others; and whether their impacts varied depending on recidivism type. Overrides were applied in 15.4% of cases, most often (94.1%) to increase risk levels. Override use varied based on gender, race, and the nature of index offenses. Based on receiver operating characteristic analyses, the results generally indicated that adjusted risk levels (incorporating professional overrides) demonstrated inferior predictive validity relative to unadjusted metrics. The results suggest a need for increased caution and consistency in the application of professional overrides.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".