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Record W4281743032 · doi:10.1177/21674795221103415

Selling vs. Supporting Motherhood: How Corporate Sponsors Frame the Parenting Experiences of Elite and Olympic Athletes

2022· article· en· W4281743032 on OpenAlexafffund
Talston Scott, Sydney V. M. Smith, Francine Darroch, Audrey R. Giles

Bibliographic record

VenueCommunication & Sport · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of OttawaCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsEliteFraming (construction)AthletesPsychologySocial psychologyEmpowermentPublic relationsPolitical scienceElite athletesAdvertisingBusinessEngineeringPoliticsMedicineLaw

Abstract

fetched live from OpenAlex

Recently, motherhood and pregnancy in elite sport have received increased attention in sport media. Through a comprehensive news media search across Factiva as well as a gray literature search using Google search engine, we analyzed 115 articles using feminist framing analysis. We developed two primary frames: 1) empowerment versus exploitation, and 2) proactivity versus reactivity. Our results show that many pregnant and parenting athletes frame their respective sponsors as exploitative for recognizing and capitalizing upon their unique marketing value, while these same corporate sponsors frame themselves as industry leaders who empower pregnant and parenting athletes. These two frames show that pregnant/parenting elite athletes commonly face discriminatory policies and practices and that there is often a lack of congruence between marketing and actual corporate practices and policies. These findings arguably reflect larger societal issues related to gender equity and highlight the importance of action over rhetoric to ensure motherhood is supported-rather than marketed-for elite athletes.

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)0.005
metaresearch head score (Gemma)0.012
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 score0.006
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.004
Scholarly communication0.0060.004
Open science0.0000.002
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 head0.051
GPT teacher head0.301
Teacher spread0.250 · 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

Citations25
Published2022
Admission routes2
Has abstractyes

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