Capturing Change: Validation of the Client Change Scale with the Correctional Service of Canada Community
Bibliographic record
Abstract
Ideally, when an individual enters the Criminal Justice System there is a belief and expectation that they will, over time and through intervention, change from the individual who perpetrated the crime(s) to a law-abiding citizen, upon being released to the community.However, there are currently few measures of justice-involved person (JIP) change that have established validity with regards to predicting post-program or post-release outcomes.The Client Change Scale (CCS) is a risk-relevant, desistance-oriented approach consistent with the Transition Model of Offender Change (Serin & Lloyd, 2009).The purpose of this research was to validate the CCS with a sample of 390 JIPs under community supervision by the Correctional Service of Canada (CSC).The mixed-method, retrospective file reviews suggest that the CCS reflects acceptable psychometric properties, predicts, and in some cases incrementally predicts, post-release outcomes.The findings suggest that the CCS also has utility with predicting supervision type (discretionary versus statutory) and differentiates based on programming assignment status.The qualitative findings suggest that the information available in the Offender Management System (OMS) at CSC is sufficient for scoring the items on the CCS, though the sources of information vary depending on the constructs.The results are promising and support prospective studies using the CCS in both programming and supervision contexts with larger samples, including women and diverse JIP samples.Taken together, the CCS appears to be a useful new assessment of change that will help decision-makers to make more defensible and accurate decisions regarding transfers to reduced security, discretionary release, programming requirements, and supervision needs.
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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 | 0.006 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".