Gender Effects in Actuarial Risk Assessment: An Item Response Theory Psychometric Study of the LS/CMI
Bibliographic record
Abstract
Actuarial tools play an important role in correctional and risk management systems as they are widely used to assess potential recidivism. In psychometric studies of the predictive value of these tools, it is, rightly or wrongly, common to find all items and all components being given the same weight such that they contribute equally to the case management of individuals and to the determination of criminal recidivism risk. Item Response Theory (IRT) allows for psychometric analysis permitting to evaluate the quality of the items in actuarial tools, such as the LS/CMI. As such, IRT methods can improve our understanding of an instrument’s psychometric properties well beyond what is already known from traditional approaches. This paper draws on IRT to explore the predictive evidence for the LS/CMI across inmates’ reported gender. The sample consisted of male (n = 1200) and female (n = 1148) inmates serving a custodial sentence. The analyses suggest that the predictive evidence for the LS/CMI is strongly related to the discrimination parameter of the items and varies considerably by gender. In conclusion, this study contributes to the understanding of criminal recidivism as a function of gender and questions the interpretation of the total score (from the section General Risk/Need Factors) generated by the LS/CMI.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".