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Record W3164963552

The Weight of Fitting the Description: Using Critical Race Theory to Explore Black and Indigenous Youth Perceptions of the Police

2021· dissertation· en· W3164963552 on OpenAlexaboutno aff
Kanika Samuels Wortley

Bibliographic record

VenueUWSpace (University of Waterloo) · 2021
Typedissertation
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsnot available
Fundersnot available
KeywordsCritical race theoryIndigenousRace (biology)PerceptionCriminologyGender studiesBlack maleSociologyPsychologySocial psychologyEcology
DOInot available

Abstract

fetched live from OpenAlex

Effective law enforcement is contingent on public support. A growing volume of research examining public perceptions of the police suggest that trust and confidence in the police is very low among youth and specific racialized populations. However, there is a gap in Canadian research that examines the complexities of the relations between racialized youth and the police. The following dissertation employs a mixed-methods approach to explore youth perceptions of the police in Canada. With a focus on a racially diverse sample of Black, Indigenous and White youth, the study aims to examine whether there are racial differences with respect to confidence in law enforcement. Using Statistics Canada’s 2014 General Social Survey (GSS) on victimization (cycle 28), the first study specifically examines Black, Indigenous and White youth’s attitude toward the police. Both bivariate and multivariate analyses suggest that race plays a significant role in identifying Canadian youth's perception of the police. Thus, in Canada, Black and Indigenous youth have lower confidence in the police compared to their White counterparts. Furthermore, a multivariate analysis suggests that gender, geographic location and previous victimization also have an impact on confidence in police. \nThe results of these findings go against Canada’s international reputation as a tolerant multicultural society. However, due to formal and informal bans on the collection of race-based data, little is known about racialized youth perceptions of police within Canada. Thus, to have a better understanding as to why Black and Indigenous youth report negative perceptions of the police, the second study employs the critical race methodology of composite counter storytelling. This approach will highlight the perspectives of Black and Indigenous youth in Toronto, Canada’s largest metropolitan city, and explore their experiences with law enforcement. This aims to counter Canada’s international status as a multicultural utopia and demonstrate how legal criminal justice actors, such as the police, perpetuate the marginalized status of Black and Indigenous youth through the process of criminalization. \nContinuing a critical race perspective, the final study explores the impact of both negative experiences and perceptions of the police among Black and Indigenous youth in Canada. The findings suggest that as a result of perceived racial bias within policing, Black and Indigenous youth are less likely to report personal violent victimization to law enforcement officials. As a result, I argue that in Canada, due to systemic racial bias within policing, both Black and Indigenous youth are at an increased risk of violent victimization, and thus furthering their vulnerability and marginalization within society. The concluding chapter explores the implications of these findings and policy recommendations for Canadian police agencies.

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 imitation

Not 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.

metaresearch head score (Codex)0.013
metaresearch head score (Gemma)0.022
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.195
Threshold uncertainty score0.388

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0130.022
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0110.025
Scholarly communication0.0080.009
Open science0.0020.004
Research integrity0.0020.006
Insufficient payload (model declined to judge)0.0040.000

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 head0.052
GPT teacher head0.308
Teacher spread0.256 · 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 source (direct Gemma or distilled Codex), 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
Published2021
Admission routes1
Has abstractyes

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