Reforming Indigenous policing: Understanding the context for change
Bibliographic record
Abstract
Protests over the policing of Black and Indigenous people and people of Colour that started after the death of George Floyd in May 2020 at the hands of the Minneapolis police set the stage for debates about the role of the Canadian police in ensuring public safety. These protests have resulted in calls for police reforms, including reallocating police funding to other social spending. The public’s attention has focused on urban policing, and there has been comparatively little focus on policing rural Indigenous communities. We address this gap in the literature, arguing that Indigenous policing is distinctively different than what happens in urban areas and the challenges posed in these places are unlike the ones municipal officers confront. We identify ten specific challenges that define the context for Indigenous policing that must be considered before reforms are undertaken. Implications for further research and policy development are identified, including founding a commission to oversee First Nations policing.
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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 | 0.009 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.046 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".