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Record W4386928705 · doi:10.1080/2159676x.2023.2259397

Whiteness, Canadian university athletic administration, and anti-racism leadership: ‘A bunch of white haired, white dudes in the back rooms’

2023· article· en· W4386928705 on OpenAlexaffabout
Braeden McKenzie, Janelle Joseph, Sabrina Razack

Bibliographic record

VenueQualitative Research in Sport Exercise and Health · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRacismOppressionWhite (mutation)SociologyPower (physics)Gender studiesCriminologyPublic relationsPolitical scienceLawPolitics

Abstract

fetched live from OpenAlex

This paper theorises ‘whiteness’ in relation to systems of power, leadership, and oppression within post-secondary sport athletic departments in Ontario, Canada. Using results from the Ontario University Athletics (OUA) Anti-Racism Project, we position whiteness as a significant and unavoidable obstacle to productive anti-racism leadership and labour within university athletics. While many Canadian university athletic departments have publicly embraced a call to anti-racism practice and policy, progress too-often remains contingent on largely white, male, and older leadership groups making decisions surrounding instances of racism that they often have no history personally experiencing, witnessing, or most concerningly, handling professionally. Examples from the project include administrators who often have more than 20-years-experience referencing ‘blindness’, naiveté, or not knowing where to look as reasons for viewing racism as a cursory or circumscribed problem, or as an issue not on the same scale as other athletic departments have attempted to tackle (e.g. sexual violence and concussion). We argue that these denials of the existence of racism work to reproduce the dominant structures of power, destabilise efforts for education or policy initiatives and maintain the oppression, racial hierarchies and marginalisation of racialised people within collegiate athletics.

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)0.018
metaresearch head score (Gemma)0.000
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 score0.421
Threshold uncertainty score0.850

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0180.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
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 head0.338
GPT teacher head0.485
Teacher spread0.147 · 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

Citations6
Published2023
Admission routes2
Has abstractyes

Explore more

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