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Record W2226444991

The Police Executive and Governance: Adapting Police Leadership to an Increase in Oversight and Accountability in Police Operations

2014· article· en· W2226444991 on OpenAlexaffabout
Gary D. Ellis

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsUniversity of Guelph-Humber
Fundersnot available
KeywordsAccountabilityCorporate governanceDemocracyPolitical sciencePublic administrationBusinessPublic relationsCriminologyLawSociologyPolitics
DOInot available

Abstract

fetched live from OpenAlex

In a democracy, it is generally understood that the police serve at the will of the people and are accountable through police governance. This usually consists of elected and/or appointed officials whose primary legal authority is to set policy and appoint the police leaders whom they hold accountable for ensuring that effective policing operations are carried out. It is widely held in common law jurisdictions that the governing body is limited in their role and cannot get involved in “operational policing issues.” In June 2010, the G20 world leaders’ conference was held in Toronto, Canada. The events surrounding the police actions during this conference caused a great deal of concern and led the Toronto Police Services Board, who are the governing authority for the Toronto Police Service, to commission a review to look at their own role. The findings in relation to “board” involvement in the operational side of policing challenged a long held belief regarding the limited role of governance in police operations. These findings will be examined in relation to the lack of board expertise and the challenges faced by police leaders to adapt and develop their attitudes, skills and abilities to respond to any expansion of governance authority.

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 imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.020
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.020
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0050.010
Scholarly communication0.0080.006
Open science0.0010.004
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0020.000

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 head0.343
GPT teacher head0.572
Teacher spread0.229 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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
Published2014
Admission routes2
Has abstractyes

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