Thirty years of research on the Level of Service Scales: A meta-analytic examination of predictive accuracy and sources of variability.
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 it