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Record W2774724821

Ontario's Bill 175 on Policing: Improved Accountability but Lagging Governance

2017· article· en· W2774724821 on OpenAlexaffabout
Kent Roach

Bibliographic record

VenueSSRN Electronic Journal · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicCriminal Law and Evidence
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAccountabilityJurisdictionCorporate governanceIndependence (probability theory)Political scienceCommunity policingCriminal justiceLaggingPublic administrationUnit (ring theory)LawPolice scienceCriminologyBusinessSociologyPsychologyMedicine
DOInot available

Abstract

fetched live from OpenAlex

This short paper examines Ontario's Bill 175 on policing. Bill 175 would implement many of the recommendations made by Justice Tulloch in his Independent Police Oversight Review including the mandatory publication of redacted investigative reports by the Special Investigations Unit in cases of police involved deaths and serious injuries that do not result in criminal charges. It raises concerns, however, the the police complaints body may be overwhelmed with the investigation of all complaints against the police and that it may have less resources for systemic reviews and that it would lose jurisdiction over complaints about policing policing. It also concludes that Bill 175 would codify an overbroad definition of police independence from governmental direction and that the enhanced accountability that Bill 175 could promote will place increased pressure on police governance and build enhanced expectations about the changes that better police oversight will make to policing. It proposes the creation of community advisory groups better to link those who review and govern the police with racialized and Indigenous 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 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)0.008
metaresearch head score (Gemma)0.030
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.110
Threshold uncertainty score0.797

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.030
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0150.009
Scholarly communication0.0120.003
Open science0.0020.004
Research integrity0.0100.005
Insufficient payload (model declined to judge)0.0080.001

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 head0.031
GPT teacher head0.330
Teacher spread0.299 · 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 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
Published2017
Admission routes2
Has abstractyes

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