Locking up those Dangerous Indians for Good: An Examination of Canadian Dangerous Offender Legislation as Applied to Aboriginal Persons
Bibliographic record
Abstract
This article examines the systemic reasons behind Aboriginal over-representation as Dangerous Offenders (DO) subject to indefinite detention. Colonialism has left behind various social traumas that continue to devastate Aboriginal communities. It is not surprising that significant numbers of Aboriginal persons accumulate lengthy violent criminal histories such that they come under the radar of the DO regime. One approach this article will stress is a call for greater emphasis on preventative social programming, and less emphasis on incarceration after the fact. This may lead to less Aboriginal over-incarceration generally, but also less Aboriginal over-representation as DOs, and less demand on resources over the long run. Secondly, the article also includes a review of case law where Aboriginal accused have been subjected to DO determinations. The conclusion is that courts are placing greater priority on the avoidance of harm to the public, to the point of marginalizing meaningful consideration of the background circumstances of Aboriginal accused, and of different approaches to long-term supervision that are grounded in Aboriginal cultures and may be more cost-effective. The article calls for greater judicial awareness and sensitivity towards the alternatives, as well as the development of an Aboriginal-specific risk assessment instrument that stresses dynamic instead of static factors.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.019 | 0.006 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 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".