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Record W4403897811 · doi:10.1080/14927713.2024.2420131

The privilege to play: race, gender, & SES advantages in boys’ high school athletic opportunities

2024· article· en· W4403897811 on OpenAlexvenueno aff
Kirsten Hextrum, Chris Knoester, James Tompsett

Bibliographic record

VenueLeisure/Loisir · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Development and Social Support
Canadian institutionsnot available
Fundersnot available
KeywordsPrivilege (computing)Race (biology)PsychologyGender studiesSociologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

In this study, with an exploratory sequential mixed-methods design, we considered the components and implications of a habitus that (re)produces racial/ethnic, social class, and gender differences in US interscholastic sport participation. We drew from independently collected qualitative (N = 19 men and 47 total college athletes) and quantitative (N = 4,097 high school boys) data and noted and investigated dynamic links between individual choices; family, community, and school contexts; and power structures that inform interscholastic athletics. Findings positioned sports as offering valuable institutionalized cultural capital but being rife with reproductive struggles. Schools serve as fields that co-construct unequal athletic opportunity structures by nurturing and rewarding a cultivated athletic habitus associated with masculinity, whiteness, and affluent dispositions. These processes situate athletic advantages and successes as purely meritorious but restrict who is most likely to receive the individual and social benefits of high school sports participation.

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)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0000.000
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 head0.061
GPT teacher head0.329
Teacher spread0.267 · 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 designObservational
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

Citations5
Published2024
Admission routes1
Has abstractyes

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