Our Health Counts: Examining associations between colonialism and ever being incarcerated among First Nations, Inuit, and Métis people in London, Thunder Bay, and Toronto, Canada
Bibliographic record
Abstract
OBJECTIVES: Indigenous peoples have a disproportionately high prevalence of incarceration in the Canadian justice system. However, there is limited Indigenous-driven research examining colonialism and the justice system, specifically associations between racism, externally imposed family disruptions, and history of ever being incarcerated. Therefore, this study examined the association between the proportion of previous incarceration and family disruption, experiences of racism, and victimization for Indigenous adults in London, Thunder Bay, and Toronto, Ontario, Canada. The three communities expressed that they did not want comparison between the communities; rather, they wanted analysis of their community to understand where more supports were needed. METHODS: Indigenous community partners used respondent-driven sampling (RDS) to collect data from First Nations, Inuit, and Métis (FNIM) peoples in London, Thunder Bay, and Toronto. Prevalence estimates, 95% confidence intervals, and relative risk were reported using unweighted Poisson models and RDS-adjusted proportions. RESULTS: Proportions of ever being incarcerated ranged from 43.0% in London to 54.0% in Toronto and 72.0% in Thunder Bay. In all three cities, history of child protection involvement and experiencing racism was associated with an approximate 25.0% increase in risk for previous incarceration. In Toronto and London, victimization was associated with increased risk for incarceration. CONCLUSION: This research highlights disproportionately high prevalence of ever being incarcerated among FNIM living in three Ontario cities. Experiencing racism, family disruption, and victimization are associated with incarceration. Decreasing the rates of family disruption, experiences of racism, and victimization should inform future policy and services to reduce the disproportionately high prevalence of incarceration for FNIM people living in urban settings.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".