Elite running, pregnancy, and parenthood: an intersectional policy analysis of World Athletics’ Gold and Platinum label road races
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.012 | 0.011 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".