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Record W4401461922 · doi:10.1177/10126902241268278

The <i>privilege</i> to do it all? Exploring the contradictions of name, image and likeness (NIL) rights for women athletes and women's sports

2024· article· en· W4401461922 on OpenAlexaff
Daniel Sailofsky

Bibliographic record

VenueInternational Review for the Sociology of Sport · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsBasketballSociologyAthletesSupreme courtPopularityGender studiesLawPolitical science

Abstract

fetched live from OpenAlex

In 2021, the US Supreme Court forced the NCAA to drop its name, image and likeness ban, allowing collegiate athletes to profit from their name, image and likeness rights. Women's basketball's biggest stars have been some of the bigger beneficiaries, profiting off of their attention, media coverage and social media presence to draw in fans, earn significant sums and grow the popularity of their sport. While name, image and likeness rights are undoubtedly a step in the right direction for campus athletic workers on their road to adequate compensation for their athletic labour, they are not a panacea. In this article, I discuss the gig-ified nature of name, image and likeness labour, and examine the intersections of name, image and likeness and gender and feminism, exploring how they might impact women's athletes’ collective power and the sustainable growth of women's sports. Using an intersectional feminist lens attuned to the racial capitalist structuring of elite sport, I explore the potential opportunities, contradictions and unintended consequences that name, image and likeness might bring for women's athletes and women's sports. These include the opportunity to parlay individual players’ success and notoriety into better working conditions for all women's basketball players, the difficulty of finding a middle ground between viewing athletes as simply commodified labour versus demanding additional, ‘off-the-court’ labour from them, and the potential pay disparities and detrimental narratives that could arise if women athletes’ success is determined by heterosexist, ‘feminine’ ‘marketability’ criteria, without adequate professional opportunities for all players. I also argue that one's views on these questions depend partially on larger normative questions related to what women's sports and individual athletes should strive for, and what counts as feminism in sport.

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)0.007
metaresearch head score (Gemma)0.006
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: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.049

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0140.041
Scholarly communication0.0150.012
Open science0.0010.007
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0050.000

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 head0.036
GPT teacher head0.337
Teacher spread0.301 · 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

Citations9
Published2024
Admission routes1
Has abstractyes

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