Robert Chrismas. Canadian Policing in the 21st Century: a Frontline Officer on Challenges and Changes
Bibliographic record
Abstract
Public police officers have a tough job. This is Robert Chrismas’s contention in his recently published Canadian Policing in the 21st Century. The book does not have much to do with Canadian policing in a broad sense. There is not a lot on the Royal Canadian Mounted Police, for instance. It is not a systematic study of municipal or provincial policing practices across Canada. Regional police services, First Nations police services, and military police are not addressed much either. What Chrismas gives the reader are interesting reflections on almost 30 years of serving in numerous high-ranking positions with the Winnipeg Police Service. Chrismas does address several topics using examples from the City of Winnipeg, Manitoba, Canada. The first chapter offers a cursory history of policing. I thought this chapter deserved more attention, especially given the colonial elements of policing in Canada, and how these continue to impact people living on the Canadian prairies. The next chapters assess the changing roles of public police and the increasing costs of crime. Chrismas then examines how technology has altered policing, and how demographic shifts have transformed the composition of police forces. There are other chapters on police training and accountability, Generation X and Y police officers, and police relations with First Nations Peoples and immigrant communities.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.025 | 0.010 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".