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Record W4200265197 · doi:10.35502/jcswb.227

Peeling the paradigm: Exploring the professionalization of policing in Canada

2021· article· en· W4200265197 on OpenAlexaffvenueabout
Kelly Sundberg, Christina Witt, Graham Abela, Lauren M. Mitchell

Bibliographic record

VenueJournal of Community Safety and Well-Being · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsMount Royal University
Fundersnot available
KeywordsCredibilityProfessionalizationScholarshipCommunity policingLaw enforcementLegitimacyAccountabilityPolice sciencePolitical sciencePublic trustPublic relationsPublic servicePublic administrationMalpracticeSociologyLawCriminal justicePolitics

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.447
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.061
GPT teacher head0.339
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2021
Admission routes3
Has abstractyes

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