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Record W4408225620 · doi:10.3138/jcs-2023-0047

Refusing Human Rights Police Partnerships

2024· article· en· W4408225620 on OpenAlexaffvenueabout
Nicole Bernhardt

Bibliographic record

VenueJournal of Canadian Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsThe Scarborough HospitalUniversity of Toronto
Fundersnot available
KeywordsHuman rightsPolitical scienceCriminologyLawSociology

Abstract

fetched live from OpenAlex

Human rights institutions play a significant but frequently neglected role in determining how the problem of anti-Black racism and policing is represented and what measures are deemed necessary to address it. The institutional distance between human rights institutions and the police themselves is an informative gauge through which to examine the contribution of human rights policy towards enhancing external monitoring of, and accountability for, racism in policing. By examining the Ontario Human Rights Commission’s (OHRC) partnership arrangements with municipal policing services in Ontario, the author demonstrates how a lack of institutional distance risks enabling anti-Blackness within Ontario policing to go unchallenged by the human rights system. Yet, despite a sustained preference for partnerships, the commission has also demonstrated the institutional capacity to say, “Nah,” to policing narratives that deny the existence of racial profiling and seek to rebrand existing policing practices as human rights compliant. By exploring the OHRC’s interventions into racism and policing in the context of the Canadian human rights system and an inequitable racial order, this article offers insights into if and how human rights law and policy can contribute to transformative anti-carceral alternatives.

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)0.001
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.647
Threshold uncertainty score0.935

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.320
GPT teacher head0.477
Teacher spread0.157 · 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

Citations0
Published2024
Admission routes3
Has abstractyes

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