Racial Disparities in Police Stops in Kingston, Ontario: Democratic Racism and Canadian Racial Profiling in Theoretical Perspective
Bibliographic record
Abstract
This study takes a quantitative and qualitative approach to examine police stops in an Ontario city. The author finds that Black residents were over stopped by police, and the over stopping may not be fully explained by the police-reported reasons and dispositions of the stops. In other words, the author suggests that police stops have less to do with crime control models of criminal justice, and more to do with surveiling marginalized populations. The author uses critical discourse analysis to examine news coverage of the racial profiling controversy in Ontario, including news reports on the study. The author argues that public discourse (both liberal 'anti-profiling' advocates and conservative supporters of police) contributes to the continued targeting of certain groups, by constructing an ideal victim of racial profiling (middle class, respectable), thus excluding all other subjects from legitimately seeking freedom from being hassled by police and having freedom of movement enjoyed by the nonprofiled population. The study also uncovers the influential role of police unions in Ontario in manipulating political discourse on race and policing.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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 teacher head, 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".