Building police legitimacy in a high demand environment: the case of Yukon, Canada
Bibliographic record
Abstract
Purpose Police legitimacy has emerged as a core concept in the study of twenty-first century policing. The purpose of this paper is to contribute new knowledge by examining the dynamics surrounding policing legitimacy in a high demand environment in Northern Canada. Design/methodology/approach A case study approach was used to explore the historical and contemporary factors that contributed to the challenges surrounding the police-First Nations relations, how these challenges affected public confidence in, and trust of, the police, and how the communities, police, and government took action to address these issues. Findings The findings reveal that it is possible for the police, First Nations, and government in high demand environments to implement reforms and to create the foundation for police-community collaboration. The development of relationships based on trust and a continuing dialogue is important components in building police legitimacy. Research limitations/implications The study was conducted in one northern jurisdiction. The findings may apply to other jurisdictions where the police are involved in policing indigenous peoples. Practical implications The case study provides insight into the processes required to fundamentally alter the police-First Nations relations, to improve police service delivery in high demand environments, and to ensure that reforms are sustained. Originality/value Police legitimacy has been examined primarily in urban environments where police services have considerable capacities and there re-extensive networks of support from various agencies and organizations. The dynamics of policing in northern communities are appreciably different and present challenges as well as opportunities for improving police legitimacy.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.043 | 0.010 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| 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".