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Record W4413233366 · doi:10.26522/ssj.v19i2.4888

Racialized Lived Experiences of No-knock Raids in Canadian Policing: Supporting the Dissenting Opinion in the Legal Case of R v Cornell

2025· article· en· W4413233366 on OpenAlexaffvenueabout
Ardavan Eizadirad, Tina Nadia Chambers

Bibliographic record

VenueStudies in Social Justice · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsUniversity of Guelph-HumberWilfrid Laurier University
Fundersnot available
KeywordsDissenting opinionPolitical scienceCriminologyLawSociology

Abstract

fetched live from OpenAlex

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.

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)0.003
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.764

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
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 head0.130
GPT teacher head0.496
Teacher spread0.366 · 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 designQualitative
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
Published2025
Admission routes3
Has abstractyes

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