Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.033 | 0.035 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.003 | 0.017 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".