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Record W4410413050 · doi:10.1177/21533687251341274

Shopping Under Suspicion in Canada: Results From a Nationwide Victimization Survey on Consumer Racial Profiling

2025· article· en· W4410413050 on OpenAlexaffabout
Shaun L. Gabbidon, Kareem L. Jordan, Akwasi Owusu‐Bempah

Bibliographic record

VenueRace and Justice · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicCrime Patterns and Interventions
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRacial profilingProfiling (computer programming)CriminologyPsychologySociologyRace (biology)Computer scienceGender studies

Abstract

fetched live from OpenAlex

This study was devoted to an examination of Canadian experiences being criminally profiled in retail stores. Using one's race or ethnicity as the primary method to identify potential shoplifters is referred to as consumer racial profiling (CRP). Relying on a national sample of Canadians, the research investigated four aspects of CRP. First, the research investigated the frequency of CRP experiences among the Canadian population. Second, the research studied the characteristics of CRP victimizations among Canadians. Third, the study also investigated whether the frequency of CRP victimization varied by race/ethnicity. Finally, the research explored whether CRP victims reported their encounter to an employee in authority following their victimization. The findings provide evidence that CRP is a national problem in Canada—with non-White racial/ethnic groups reporting the highest levels of victimization. In addition, despite their negative CRP experiences, very few victims decide to pursue remediation. The implications of the results along with future research directions are also discussed.

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)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.091
Threshold uncertainty score0.216

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.047
GPT teacher head0.352
Teacher spread0.305 · 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 designObservational
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

Citations2
Published2025
Admission routes2
Has abstractyes

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