MétaCan
Menu
Back to cohort
Record W4378530504 · doi:10.1080/21640629.2023.2218265

White privilege in Canadian high school sport: investigating white coaches’ perspectives on social justice issues

2023· article· en· W4378530504 on OpenAlexafffundabout
Evan Bishop, Stéphanie Turgeon, Wesley Tang, Tarkington J. Newman, Leisha Strachan, Corliss Bean, Martin Camiré

Bibliographic record

VenueSports Coaching Review · 2023
Typearticle
Languageen
FieldHealth Professions
TopicPhysical Education and Pedagogy
Canadian institutionsBrock UniversityUniversity of ManitobaUniversité du Québec en OutaouaisUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWhite privilegeWhite (mutation)Privilege (computing)CoachingOppressionEconomic JusticeSociology of sportSocial justiceIdentity (music)Power (physics)Gender studiesSocial identity theorySocial psychologySociologyPsychologyRacismCriminologyPolitical scienceLawSocial groupPolitics

Abstract

fetched live from OpenAlex

Sport constitutes an important setting in which to study whiteness given ongoing issues related to power, privilege, and oppression. The purpose of the study was to examine white privilege in Canadian high school sport by investigating white coaches’ perspectives on social justice issues. A total of 463 high school coaches who self-identified as white completed an online survey. Results showed how coaches who had a greater awareness of white privilege in society had more favourable attitudes towards social justice, higher importance attributed to climate change issues, greater awareness of prejudicial attitudes against the LGBT community, and a higher propensity to engage in antiracist behaviours. Moderating effects for gender identity were also found. Moving forward, white privilege should continue to be studied in coaching to better understand how it is entangled with social justice.

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)0.003
metaresearch head score (Gemma)0.004
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: Empirical
Teacher disagreement score0.176
Threshold uncertainty score0.354

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0060.003
Scholarly communication0.0040.001
Open science0.0010.002
Research integrity0.0010.001
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 head0.098
GPT teacher head0.459
Teacher spread0.361 · 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

Citations8
Published2023
Admission routes3
Has abstractyes

Explore more

Same venueSports Coaching ReviewSame topicPhysical Education and PedagogyFrench-language works237,207