Shining Light on the Dark Places: Addressing Police Racism and Sexualized Violence against Indigenous Women and Girls in the National Inquiry
Bibliographic record
Abstract
Canada has had a long-standing problem with both societal and institutional racism against Indigenous peoples, especially within the justice system. Numerous national inquiries, commissions, and investigations have all concluded that every level of the justice system has failed Indigenous peoples. More recent inquiries indicate that racism against Indigenous peoples is particularly problematic in police forces in Canada. Yet, despite the evidence, little has been done in Canada to act on the recommendations. This has resulted in the over-incarceration of Indigenous peoples, numerous deaths of Indigenous peoples in police custody, and the national crisis of thousands of murdered and missing Indigenous women and girls. This article seeks to highlight the lesser-known problem of police-involved racialized and sexualized abuse and violence against Indigenous women and girls as a root cause of the large numbers of murdered and missing Indigenous women and girls in Canada. It is argued that an in-depth look at police-involved disappearances, sexual assaults, and murders of Indigenous women should be included in a national inquiry into the high rates of murdered and missing Indigenous women and girls. It is hoped that such an investigation under the national inquiry will result in evidence-based analysis and recommendations for legislative and policy-based changes that are consistent with the human rights protections afforded Indigenous women and girls and with the calls for action by Canada's Truth and Reconciliation Commission, various United Nations human rights bodies, and the families, communities, and nations of the Indigenous victims.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.039 | 0.022 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.004 | 0.008 |
| 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".