Policing isolated Aboriginal communities: perspectives of Canadian officers
Bibliographic record
Abstract
Purpose The purpose of this paper is to compare the self‐reported attitudes toward Aboriginal policing of officers in isolated/remote communities with those from accessible Aboriginal communities. Design/methodology/approach Survey results are reported for 294 officers working in Aboriginal communities throughout Canada. Findings Officers working in remote jurisdictions tended to be younger, better educated, and had less policing experience than those working in non‐isolated Aboriginal communities. Social problems in these remote communities were thought to be more serious than those reported by officers working in accessible Aboriginal communities. Despite these demographic and contextual differences, self‐reported attitudes toward Aboriginal policing were generally similar, although officers working in isolated communities tended to report more conservative views on enforcement. Research limitations/implications The generalizability of the results is limited by the modest (40.7 percent) response rate, and the fact that officers working in Aboriginal agencies were less likely to participate than those working for national or provincial police services. Practical implications The results suggest that more effective recruiting, training, and retention strategies should be developed for officers working in Aboriginal communities. Originality/value This is the first comprehensive study of the perceptions of officers toward Aboriginal policing. While the police in isolated communities represent a small proportion of all law enforcement officers, their insight sheds light on this often misunderstood and under‐researched aspect of law enforcement.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.006 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".