Bibliographic record
Abstract
This dissertation represents an application of Data Pattern Analysis methods to offender risk assessment in criminal justice. The purpose of this dissertation is to provide a set of confirmatory factor analyses of the Level of Service Inventory-Revised (LSI-R; Andrews & Bonta, 1995). The LSI-R is a widely used offender risk/needs assessment with 54 dichotomous items broken down into 10 subscales. The LSI-R is a “dual purpose” “risk/needs” offender assessment instrument that is designed to assess 1) recidivism risk and 2) treatment needs. The first purpose of the LSI-R, assessment of recidivism risk, is achieved by examining the total LSI-R score, which is the sum of the 54 dichotomous item scores. Higher scores are correlated with higher recidivism levels. The second purpose of the LSI-R, assessment of treatment needs, is achieved by the examination of the ten LSI-R subscale scores. A high score on a subscale indicates that the offender may need treatment related to that particular domain (employment, attitude, drug use, etc.). Previous analyses suggest that the subscale structure of the LSI-R is not an accurate representation of how the items are grouping into factors. The results from three previous item level exploratory factor analyses of the LSI-R suggest that the factor structure of the LSI-R does not match the subscale structure. This dissertation provides a set of confirmatory factor analyses of the LSI-R using 3,493 LSI-R assessments collected from male offenders while they were on probation in a Midwestern county from 2002 to 2006. The initial confirmatory factor analysis of the LSI-R using the subscales as factors produced a Comparative Fit Index (CFI) of only .760, which suggests that the subscale structure does not provide a good fit to the item covariance structure of the LSI-R. After 29 modifications, a confirmatory factor analysis model was produced with a CFI of .950, which indicates a good fitting model. The 29th model had 19 factors and 11 items that loaded onto multiple factors. These results suggest that further analyses and discussions of the LSI-R item covariance structure are needed. References Andrews, D.A., & Bonta, J. (1995). The Level of Service Inventory-Revised. Toronto: Multi-Health Systems.
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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 | 0.033 | 0.118 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".