Quality assurance in Canadian police services
Bibliographic record
Abstract
Purpose The purpose of this study is to investigate the presence of quality assurance, risk management, and audit practices at the municipal, provincial and federal levels of law enforcement in Canada. Based on open‐ended interviews and surveys with management of law enforcement agencies, the study attempts to determine the extent to which these practices are in place, the structure of managing these functions, including the tools that are used to do so and the role of these functions within the organization. Design/methodology/approach The survey was sent to 104 police services, which are represented in the Canadian Association of Chiefs of Police. A total of 30 people responded on behalf of 23 police agencies. The survey was supplemented with in‐depth interviews with selected police services. Findings There was a high level of consensus around the reasons for undertaking these processes and the rankings were also remarkably consistent. The way in which risks are defined varies from organization to organization but some common patterns emerge. The top risks are those associated with the external environment (80.77 per cent) and operational risks (76.92 per cent). Originality/value The study confirmed the challenges associated with establishing rigorous professional standards, while balancing the interests of different stakeholders in the development and application of the process.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.014 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".