Developing and Validating a Risk Assessment Scale to Predict Inmate Placements in Administrative Segregation in the Correctional Service of Canada
Bibliographic record
Abstract
Concern over the use of administrative segregation has motivated efforts to reduce segregation placements.The purpose of this study was to develop and validate an actuarial risk assessment scale to predict admissions to administrative segregation (for reasons of jeopardizing security or the inmate's own safety) for at least six consecutive days within two years of admission to the Correctional Service of Canada (CSC).The sample (N = 16,701) included all male offenders admitted to CSC from fiscal years 2007/2008 through 2009/2010 and all female offenders admitted from 1999/2000 through 2009/2010.Offenders were randomly divided into a development sample (N = 11,110) and a validation sample (N = 5,591).Analyses were separated by reason for administrative segregation, gender, and Aboriginal ancestry.Overall, 413 potential predictor variables were examined, including items from assessment scales, demographic information, current offence information, flags/alerts/needs, and information from previous federal sentences.Approximately 24% of offenders were placed in administrative segregation.Of the 413 variables examined, 86% significantly predicted segregation placements.The item pool was reduced using Principal Components Analysis, tests of unique contributions within the measured components, and considerations of general utility and face validity.Several scales were developed and validated.Considering both accuracy and efficiency, the optimal scale had six static items (age, prior convictions, prior segregation placement, sentence length, criminal versatility, and prior violence) -this scale was called the Risk of Administrative Segregation Tool (RAST).Attempts to develop scales unique for men and women and those of Aboriginal ancestry did not yield meaningfully higher accuracy than the overall RAST.The RAST generalized well to the validation sample (AUC = .80)with high discrimination, suitable calibration (mostly non-significant E/O indexes), and superior performance to other risk scales used by CSC.Normative data (absolute segregation rates, percentiles, and risk ratios) were presented for the RAST.The RAST is an appropriate scale to use in practice for identifying risk of administrative segregation placements among CSC inmates and may serve as a first step in future efforts to divert offenders from segregation.Limitations of the current study and suggestions for future research are discussed.iii
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".