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Record W4416061213 · doi:10.1080/19406940.2025.2583982

Elite running, pregnancy, and parenthood: an intersectional policy analysis of World Athletics’ Gold and Platinum label road races

2025· article· en· W4416061213 on OpenAlexafffund
Talia Ritondo, Francine Darroch, Audrey R. Giles

Bibliographic record

VenueInternational Journal of Sport Policy and Politics · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsCarleton UniversityUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsElitePolicy analysisPublic policyRace (biology)Corporate governance

Abstract

fetched live from OpenAlex

In 2018, 50.6% of registered participants in mass participation running events were women. However, gender disparities remained in that, compared to men, women stopped racing at a younger age and raced shorter distances. These differences may exist due to gendered discourses of pregnancy and parenting and a lack of accommodation for self-identifying women, transgender and non-binary (SWTNB) pregnant and parenting athletes (PPA) in community and elite sport. Our purpose for engaging in this critical commentary is to analyse World Athletics’ road race policies for discourses concerning SWTNB PPAs’ participation. Specifically, we used intersectionality-based policy analysis to examine how the needs of SWTNB PPAs are considered (or not) when registering for and participating in an internationally ranked road race. We analysed 52 World Athletics Gold and Platinum Label races. Three principal discourses were reproduced through various policies, which reinforced gendered, patriarchal, classist, and ableist discourses regarding SWTNB PPA’s participation: i) race registration accommodations for PPAs are already in place, ii) welcome runners are mentioned in policies, and iii) pregnancy and parenting policies are ‘inclusive’. By examining these discourses, we highlight how race directors and policymakers can not only create intersectional accommodations for SWTNB PPAs but also support and protect their rights to participate.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.636
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.026
GPT teacher head0.367
Teacher spread0.340 · 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 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

Citations1
Published2025
Admission routes2
Has abstractyes

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