Taking Down the Straw Man: A Reply to Webster and Doob
Bibliographic record
Abstract
Introduction The Custody Rating Scale (CRS) was found by Blanchette, Verbrugge, and Wichmann (2002) to be a valid measure of custodial risk for federally sentenced women offenders. CRS validation includes, but is not limited to, its role in predicting institutional adjustment, escape, and reoffending. Nevertheless, in the July 2004 issue of this journal Webster and Doob (2004) question the validity of the CRS for women, primarily because Blanchette et al.'s (2002) study revealed that one of its sub-scales does not predict institutional misconduct. The fact that scores on the Institutional Adjustment (IA) sub-scale of the CRS correlated positively and significantly with violent (r = 0.39) and non-violent (r = 0.47) incidents while in custody indicates that it is a valid measure of institutional risk. The fact that the Security Risk (SR) subscale did not correlate to institutional misconduct is not surprising, as that sub-scale was designed to measure public safety risk. By setting up this straw man, Webster and Doob (2004) demonstrate that their views are misdirected and reflect a narrow understanding of the application and validation of security classification instruments. The framework for our discussions is to challenge the investigation of the CRS presented by Webster and Doob, who provide no new empirical data on the instrument per se and draw some erroneous conclusions. Rather than entering into a theoretical debate about security classification, we have chosen to take down their straw man by presenting the legal and empirical underpinnings of the CRS, offering new validation data on the instrument, and addressing other contentious issues. Legal and empirical underpinnings Unfortunately, Webster and Doob are misinformed when they state that the CRS constitutes the foundation of the security classification system in (2004: 396). In fact, it is s. 30 of the Corrections and Conditional Release Act (CCRA) that legally prescribes how security classification decisions are made. Furthermore, in accordance with ss. 17 and 18 of the Corrections and Conditional Release Regulations (CCRR), the Correctional Service of Canada (CSC) is mandated to take the following factors into consideration in assigning a security classification of maximum, medium, or minimum: (a) seriousness of the offences committed by the inmate; (b) any outstanding charges; (c) the inmate's performance and behaviour while under sentence; (d) the inmate's social, criminal, and, where available, young offender history; (e) any physical or mental illness or disorder suffered by the inmate; (f) the inmate's potential for violent behaviour; and (g) the inmate's continued involvement in criminal activities. In the field of corrections, the purpose of security classification is to protect the public, employees, and offenders through appropriate placement of the inmate. As is true of most classification instruments, CRS development began with a review of existing instrumentation being used in other jurisdictions and proceeded to empirically test common factors used to assign custody levels (Canada, Solicitor General 1987). Not surprisingly, certain factors were common to all these scales: severity of current offence, sentence length, violent criminal background, and various indices of behaviour prior to incarceration. More importantly, the background documentation for the CRS includes descriptions of classification models, two-dimensional in nature, that take into consideration both institutional risk and public risk. In these classification models, scores are integrated (in a matrix fashion) to arrive at a final custody designation. In a similar way, the CRS was designed with two dimensions: an Institutional Adjustment (IA) sub-scale (5 items: history of involvement in institutional incidents, escape history, street stability, alcohol/drug use, age at time of sentencing) and a Security Risk (SR) sub-scale (7 items: number of prior convictions, most severe outstanding charge, severity of current offence, sentence length, street stability, prior parole and/or mandatory supervision/statutory releases, age at time of first federal admission). …
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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 | 0.029 | 0.123 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.011 | 0.030 |
| Open science | 0.010 | 0.008 |
| Research integrity | 0.082 | 0.139 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".