MétaCan
Menu
Back to cohort
Record W2318871706 · doi:10.1093/police/pat038

Robert Chrismas. Canadian Policing in the 21st Century: a Frontline Officer on Challenges and Changes

2013· article· en· W2318871706 on OpenAlexaffabout
Kevin Walby

Bibliographic record

VenuePolicing A Journal of Policy and Practice · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicCanadian Identity and History
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsOfficerAccountabilityPolice sciencePolitical scienceImmigrationCommunity policingCriminologyService (business)Metropolitan policePublic administrationSociologyLawCriminal justiceBusiness

Abstract

fetched live from OpenAlex

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.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.925
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.302
Teacher spread0.273 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2013
Admission routes2
Has abstractyes

Explore more

Same venuePolicing A Journal of Policy and PracticeSame topicCanadian Identity and HistoryFrench-language works237,207