Intersection of Indigenous Peoples and Police: Questions about Contact and Confidence
Bibliographic record
Abstract
Despite much anecdotal, journalistic, and statistical evidence of their oppression by colonial and neocolonial police practices, little is known about Indigenous peoples’ attitudes towards the police in Canada. The theory that involuntary police–citizen contacts increase citizens’ mistrust, fear, and dissatisfaction and, ultimately, decreases confidence in the police was advanced. Hypotheses arising from this historical-theoretical context were tested with the 2014 panel of Canada’s General Social Survey, including 951 Indigenous (First Nations, Métis, or Inuit) and 21,576 non-Indigenous white participants. Indigenous identity and involuntary contacts were both significantly associated with a lack of confidence in police, p < .001. As hypothesized, the odds associated with involuntary contacts (odds ratio [OR] = 2.66) were stronger than those associated with being Indigenous (OR = 1.81). While the hypothesized ethnicity by contact interaction was not observed, Indigenous participants (5%) were two and a half times as likely as non-Indigenous white participants (2%) to have had relatively frequent (two or more) involuntary contacts with the police during the past year. Therefore, at the population level Indigenous people are at much greater risk of coming into involuntary contact with the police and of consequently lacking confidence in police. Policy implications and future research needs are discussed.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.005 |
| 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".