Aboriginal over-representation in the criminal justice system: A tale of nine cities
Bibliographic record
Abstract
This paper explores the contribution certain large Canadian cities may make to the over- representation of aboriginal people in the criminal justice system. The nine cities under study are large urban areas known in Statistics Canada terms as Census Metropolitan Areas (CMA's). The cities are located in eight provinces, and represent Western, Prairie, Eastern and Atlantic Canada. For the analyses, a variety of Statistics Canada and Department of Indian Affairs and Northern Development (DIAND) data, as well as Canadian Centre for Justice Statistics (CCJS) and Correctional Services Canada (CSC) data on aboriginal offenders and over-representation and other aboriginal criminal justice research, were analysed. The paper explores a number of theoretical concepts such as social disorganization, social learning theory, and posits others to understand the urban reality for aboriginal populations and, from that, regional variation in over-representation. Prairie cities appear to contribute disproportionately to the over-representation problem and advantage and disadvantage are disproportionately distributed in urban centres across the country. The nine cities are grouped into high, medium and low "contribution to over-representation" cities based on the demographics of their aboriginal populations. The paper suggests that more research is required to understand how advantage and disadvantage are bestowed on reserve and, by implication, on urban aboriginal populations.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.026 | 0.013 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".