The Use of Meta-Analysis to Compare and Select Offender Risk Instruments: A Commentary on Singh, Grann, and Fazel (2011)
Bibliographic record
Abstract
Criminal justice researchers and agencies often look to empirical reviews and meta-analyses when evaluating offender risk instruments. However, variations in meta-analytical methods can influence the interpretation of statistical findings, resulting in potentially misleading conclusions. Through our re-examination of a meta-analysis by Singh, Grann, and Fazel ( 2011 ), we outline common methodological problems that may occur and demonstrate how alternative interpretations might be derived. We conclude by providing recommendations for conducting meta-analyses. Suggestions are made for researchers to consider alternative strategies when examining the validity of risk instruments, and for correctional agencies to consider, but go beyond, predictive validity when selecting offender risk instruments.
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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 | 0.171 | 0.498 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.007 | 0.011 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.008 | 0.016 |
| Open science | 0.012 | 0.006 |
| Research integrity | 0.038 | 0.056 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".