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Record W4235051269 · doi:10.32920/ryerson.14653818

In their shoes: exploring the social experiences of Black male police officers in Canada

2021· preprint· en· W4235051269 on OpenAlexaffabout
Anthony Lawrence

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicPolicing Practices and Perceptions
Canadian institutionsToronto Metropolitan UniversityCentre for Social Innovation
Fundersnot available
KeywordsOfficerCriminologyRace (biology)Qualitative researchIdentity (music)Critical race theoryRacismGlass ceilingCommunity policingSocial identity theoryBlack maleRacial profilingSociologyGender studiesSocial psychologyPsychologyPolitical scienceSocial groupLawSocial science

Abstract

fetched live from OpenAlex

This qualitative research study presents a critical analysis of race and policing by examining the experiences of four Black male police officers in Canada. This study seeks to understand the essence of these experiences and understand the reality of what it means to be a Black male police officer. Included are the results of qualitative interviews with these police officers, using critical race theory as the theoretical framework to explain participants’ experiences as police officers. The themes that emerged from the interviews were the following: the glass ceiling for Black police officers; issues of identity and belonging; negative stereotyping; and future recommendations. Given that this group belongs to both a profession which exhibits inherent racial bias, in the form of over-surveillance and the use of excessive (and even lethal) force against racialized minorities, as well as belonging to the very minority community targeted by the police, it is imperative that we explore and understand the unique tensions black officers experience.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.134
GPT teacher head0.372
Teacher spread0.238 · 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.

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

Citations1
Published2021
Admission routes2
Has abstractyes

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