Examining the judicial imposition of indeterminate sentences for dangerous offenders in Canada
Bibliographic record
Abstract
Part XXIV of the Criminal Code contains a legislative mechanism to detain indefinitely people who have repeatedly committed violent offences and who are deemed too dangerous to be released into society because of their history of violent offending. Sentencing under Part XXIV involves judicial consideration of statutorily mandated risk assessment reports. These reports are conducted by psychological experts who present their testimony surrounding their report in a DO hearing. Judges rely heavily on the information contained within these reports when deciding whether to impose an indeterminate sentence on an individual who has been designated dangerous. Despite being challenged over time, the DO regime has been upheld as constitutional. Notwithstanding, there is a growing body of research questioning the socio-cultural validity of Part XXIV’s sentencing mechanism, specifically its great emphasis on predictions of future risk. The purpose of this thesis is to examine how and why judges decide to impose and indeterminate sentences on certain individuals designated dangerous, while others not. I first question whether indeterminate sentences, as a practice, can be theoretically justified. Through examining caselaw I look at how judges determine the appropriate disposition for designated dangerous offenders, and the factors which judges appear to give the most weight in deciding whether to impose an indeterminate sentence. Specifically, I examine the impact that offender/victim relationships had on disposition outcome, and how judges consider the Indigeneity of the offender in assessing whether the indeterminate sentence is appropriate. Ultimately, I flag the need for further research into cultural bias in the context of risk assessment under Part XXIV and how judges activate their remedial role by adopting a ‘Gladue forward approach’ and refusing to impose indeterminate sentences on Indigenous people.
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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 | 0.010 | 0.064 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.025 | 0.006 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.002 | 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".