Bibliographic record
Abstract
Citizens and advocacy groups across Canada have called for an end to street checks, a practice that involves the police stopping and questioning people on the street, absent grounds for arrest or detention, to collect identifying information. Across jurisdictions, the data reveals that street checks disproportionately target Black, Indigenous, and other racialized and marginalized persons. Police departments have historically justified these racial disparities by framing street checks as a proactive policing tool, but in recent years, the rhetoric around street checks has shifted. Now, street checks are a way for officers to check in on the “well-being” of marginalized community members. In Vancouver, the VPD has framed this practice as promoting a social good, but this article contends that well-being checks are another manifestation of arbitrary street checks. This article first examines how street checks and the discourse surrounding them have evolved in Toronto, leading to the current moment, where departments face mounting pressure to justify racial disparities in their data. Next, this article shifts its focus to the Downtown East Side (DTES) of Vancouver, where police are facing a similar public reckoning, and have responded with one specific, novel justification: street checks are justifiable as a proactive policing tool that protects the interests of society’s most vulnerable. This article concludes by arguing that well-being checks may function as a new manifestation of discriminatory policing, one that responds to a specific history and context but duplicates the experience of an arbitrary street check.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.245 | 0.131 |
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".