Thirty years of research on the Level of Service Scales: A meta-analytic examination of predictive accuracy and sources of variability.
Bibliographic record
Abstract
We conducted a comprehensive meta-analysis of the Level of Service (LS) scales, their predictive accuracy and group-based differences in risk/need, across 128 studies comprising 151 independent samples and a total of 137,931 offenders. Important potential moderators were examined including ethnicity, gender, LS scale variant, geographic region, and type of recidivism used to measure outcome. Results supported the predictive accuracy of the LS scales and their criminogenic need domains for general and violent recidivism overall, and among broad subgroups of interest, including females and ethnic minorities. Although results indicated that gender and ethnicity were not substantive sources of effect size variability, significant differences in effect size magnitude were found when analyses were conducted by geographic region. Canadian samples consistently demonstrated the largest effect sizes, followed by studies conducted outside North America, and then studies conducted in the United States. This pattern was observed irrespective of gender, ethnicity, LS domain, LS variant, or type of recidivism outcome, suggesting geographic region may be an important source of effect size variation. We discuss possible factors underlying this pattern of results and identify areas for future research.
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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 | 0.039 | 0.093 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.007 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".