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Record W4308891649 · doi:10.1123/jpah.2022-0288

Racialized Women in Sport in Canada: A Scoping Review

2022· review· en· W4308891649 on OpenAlexaffabout
Janelle Joseph, Bahar Tajrobehkar, Gabriela Estrada, Zeana Hamdonah

Bibliographic record

VenueJournal of Physical Activity and Health · 2022
Typereview
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsSociology of sportGender studiesPsychologyGerontologyMedicineSociology

Abstract

fetched live from OpenAlex

BACKGROUND: This scoping literature review examines: What literature exists about the sport and physical activity experiences of racialized cis and trans women, adolescents, and girls in Canada? METHODS: English language peer-reviewed articles, book chapters, and gray literature published January 1, 2000, up to May 31, 2020, were examined. The databases used were SPORTDiscus via EBSCO, Sociological Abstracts, Sport Medicine and Education Index, and Google Scholar. The 42 studies and 15 gray literatures found included 1430 participants explicitly specified as racialized women/girl participants. RESULTS: There was a paucity of literature on the topic overall with none (n = 0) focused on experiences of racialized trans women. The limited research notes some successful programs that address racialized women's needs. However, the research also shows widespread experiences of discrimination against women based on racial group and language and limited access to culturally relevant or welcoming sporting opportunities, such as women-only programs and spaces. CONCLUSIONS: Much more research should be done to disaggregate "immigrants" into specific racial and ethnic groups, attend to intersectional identities and barriers, understand a wide range of involvement (eg, including coaching, high performance sport, recreation, exercise, university sport, mentorship programs), document racism and White privilege, and describe the joys of participation in sport for racialized women.

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)0.005
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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.941
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.140
GPT teacher head0.451
Teacher spread0.312 · 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 designOther design
Domainnot available
GenreReview

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

Citations19
Published2022
Admission routes2
Has abstractyes

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