A Look at the Difficulty and Predictive Validity of LS/CMI Items With Rasch Modeling
Bibliographic record
Abstract
The current study aimed to provide data on the performance of items, dimensions, and the total score of the Level of Service/Case Management Inventory (LS/CMI), one of the most internationally used actuarial scales for the prediction of general recidivism in convicted persons. Using the full population of Quebec’s male incarcerated population evaluated between 2008 and 2015 with a 2-year follow-up (N = 15,961), results indicated that the predictive validity of the scale and its components was in line or better than effect sizes reported in other validation studies. A Rasch model was computed to obtain the difficulty parameter of LS/CMI items. Results indicated that items had varying levels of difficulty and covered the whole spectrum of the risk continuum. However, difficulty in Rasch was uncorrelated with the predictive validity of items, which casts a doubt on the applicability of some aspects of item response theory to actuarial scales.
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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 | 0.050 | 0.148 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".