Racialized Lived Experiences of No-knock Raids in Canadian Policing: Supporting the Dissenting Opinion in the Legal Case of R v Cornell
Bibliographic record
Abstract
A no-knock police raid is a law enforcement tactic where officers enter a private dwelling without prior notice. In Canada, there is a significant lack of comprehensive data, on the frequency and outcomes of no-knock police raids and their unintended damages and consequences. While quantitative studies on police violence have been informative, there is a significant gap in documenting racialized lived experiences with no-knock police raids in Canada. This research addresses the gap by focusing on the lived experiences of four Black and one South Asian individual subjected to no-knock police raids. Qualitative interviews were conducted in 2022 followed with thematic analysis. This exploratory study, though small in sample size, sheds light on the overlooked experiences of individuals subjected to no-knock police raids. It provides data to support the dissenting opinion in the legal case R v Cornell which advocates for the regulation of Special Weapons and Tactics (SWAT) teams including controls on no knock tactics in Canada. The findings contribute to understanding the emotional and psychological toll no-knock police raids have on racialized individuals and communities. Findings contribute to the broader literature and discussions on how to improve policing tactics to mitigate harm by preventing unintended collateral harm and better protect privacy rights.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.045 | 0.025 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".