Managing High Risk Offenders: An Evaluation of Preventative Detention Legislation in Canada
Bibliographic record
Abstract
The purpose of the present research was to evaluate preventative detention legislation in Canada by a) comparing designated offenders to non-designated offenders in terms of risk and comparing both designated groups in terms of treatment amenability, b) examining the risk assessment reports that informed the judges' decisions by comparing prosecutionretained versus court-appointed expert assessments, and c) evaluating the reasons for sentencing to assess the variables most important to the final designation outcome.The sample sizes for the three studies were as follows: Study 1 = 58 dangerous offenders (DOs), 129 long-term offenders (LTOs), and 562 flagged offenders; Study 2 = 43 prosecutionretained expert assessments, 68 court-appointed expert assessments; Study 3 = 31 DO decisions and 55 LTO decisions.From Study 1 it can be concluded that the preventative detention legislation appears to be correctly applied in that a) DOs and LTOs were a higher risk group compared with flagged offenders, b) DOs and LTOs were similarly rated as high risk on validated risk measures, and c) DOs and LTOs differed on indices of treatability and risk management.Results from Study 2 demonstrated that across all comparisons, risk assessment reports between prosecution-retained versus court-appointed experts were more similar than they were different.Results also demonstrated that court-appointed experts were more likely than prosecution-retained experts to list risk factors and to discuss managing the offender's risk in the future.Results from Study 3 demonstrated that judges' decisions were consistent with expert assessments in terms of risk, treatment amenability, and risk management.Experts' ratings of treatment amenability and risk management were also significant predictors of the designation outcome indicating that judges rely on this information in making their final I would first like to thank my committee members Shelley Brown and Ralph Serin for their thoughtful comments, time, and support.I would also like to thank my internal examiner Diana Majury and external examiner, Kirk Heilbrun, for being a part of this committee; thank you for the comments and lively discussion.The completion of this thesis would not have been possible without the ongoing encouragement and support from my supervisor and mentor, Adelle Forth.Thank you for providing me with the freedom to pursue this research and for all of your feedback through this long process and the multiple drafts.Thank you for encouraging me to publish the results and to aim high.The data for this thesis were largely collected from National Flagging System (NFS) coordinators across Canada as part of a larger evaluation of the NFS being conducted at Public Safety Canada
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".