Are Conditional Sentence Orders Used Differently for Indigenous Offenders? A Comparison of Sentences and Outcomes in Canada
Bibliographic record
Abstract
Conditional sentence orders (CSOs) were introduced in Canada in 1996, largely as a mechanism to address the overreliance on incarceration. This sentencing option is particularly relevant for Indigenous individuals who have been vastly overrepresented in custodial settings. Despite being in place for over twenty years, little is known about the use and effectiveness of CSOs. The current study examined the use and outcomes of CSOs for Indigenous (n = 749) and non-Indigenous offenders (n = 1,625) in one Canadian province. Specifically, the length of CSO, frequency and type of optional conditions, number of breaches, and rates of reoffending were compared between the two groups. Results from a logistic regression, controlling for risk relevant co-variates, indicated that Indigenous individuals tended to receive shorter CSOs compared to Caucasian individuals. However, Indigenous individuals were 35% more likely than Caucasian individuals to be convicted of a breach and more likely to incur multiple breaches while on a CSO. Despite the differences in the rates of breaches, the likelihood of reoffending over a two-year period was equivalent across the two groups. Although the reasons for breach were not available for the current study, future research should investigate this further to determine whether increased breach rates for Indigenous individuals are the result of more rule-violating behaviour, an inequity in the fairness of the conditions applied to Indigenous versus Caucasian individuals, or whether it is possible that breaches are over-detected and convicted at a higher rate for Indigenous individuals. Greater awareness of the underlying mechanisms related to increased breach rates affords the opportunity to ensure that CSOs are consistently implemented, something which will contribute to achieving the objective of successful diversion from imprisonment.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".