Decolonizing Municipal Policing: Indigenous Discrimination and Institutional Approaches
Bibliographic record
Abstract
For decades, there have been growing calls to address systemic Indigenous racism in Canadian police institutions. However, progress in this area has remained troublingly slow as recent movements have had little impact on institutional reform. Indigenous Peoples are left disproportionately victimized and overrepresented in the criminal justice system due to discriminatory policing practices. In recent years calls for institutional reforms have been amplified with the completion of the National Inquiry into Missing and Murdered Indigenous Women and Girls as well as countless other scathing reports from oversight bodies into racism within municipal police services. Given this newfound urgency, municipal police services have begun to explore new ways to reconcile their relationship with Indigenous Peoples. Although, there is no standard approach to guide this new challenge of institutional decolonization within policing.\nThis paper explores how institutional approaches contribute to the maintenance and reduction of systemic racism within police agencies. Using comparative case studies on the institutional approaches of the Nishnawbe Aski Police Service and the Thunder Bay Police Service, this paper attempts to outline a standard through which municipal police services can more effectively address systemic racism and reconcile their relationships with Indigenous Peoples. The approach used by the Nishnawbe Aski Police Service demonstrates that municipal police services would benefit from incorporating community-based and Indigenous perspectives and approaches to reduce systemic racism and move toward institutional decolonization.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.038 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.002 | 0.003 |
| 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".