Peeling the paradigm: Exploring the professionalization of policing in Canada
Bibliographic record
Abstract
Maintaining public trust, legitimacy, and credibility in a constantly evolving society has proven challenging for police in the 21st century. Rising public concerns regarding police accountability are driving the need to advance the paradigm of policing by reassessing the organizational structure of law enforcement in Canada. Supported by research identifying primary directives for maintaining public trust, this proposal argues that the time has come for policing to evolve from an occupation into a formal profession. Just as any other occupation that has advanced into a profession, provincial regulatory colleges of policing should be formed with the key objective of protecting the public from malpractice and malfeasance. A provincial college of policing would allow for (a) sustained and inclusive recruitment strategies, (b) foundational knowledge of the scholarship of policing, (c) evidence-based academy training, (d) mandatory ongoing (in-service) police education, and (e) expert, objective, community-focused, independent oversight. This proposal uses characteristics of the College of Policing in England and Wales as a guiding framework for the support and preparation of professionalizing policing in Canada.
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 | 0.006 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.034 | 0.012 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.004 |
| 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".