Exposing a Motherhood Penalty in Sport: A Feminist Narrative Inquiry of Media Stories of Canadian Athlete Mothers’ Journeys to the 2020 Tokyo Games
Bibliographic record
Abstract
Coinciding with athlete mothers' stories gaining media visibility, sport media researchers are studying media discourses to learn more about socially constructed motherhood and sport. The present study extends media research on elite athlete mothers, by using feminist narrative inquiry to interrogate discrimination meanings in sport. North American sport media stories were collected on Canadian athletes' (i.e., boxer Mandy Bujold, basketball player Kim Gaucher) journeys to the 2020 Tokyo Games after being discriminated against due to their motherhood status. Thematic narrative analysis of 103 stories identified three narrative motifs (i.e., recurring concepts) in stories linked to discrimination meanings: last shots, forced to choose, and more than us. The first two motifs are discussed in relation to a motherhood penalty narrative linked to sexism and discrimination. The more than us motif is discussed in relation to the resolution to compete for both athletes, linked to maternal activism and social change. All three motifs exposed and challenged maternal discrimination in sport, using 'feminist consciousness' linked to a neoliberal feminist status quo. These findings show the pedagogical potential of media stories for athlete maternity rights awareness and structural change, while highlighting a need for intersectional feminist reform regarding athlete parents and post-pandemic recovery.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.019 | 0.018 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| 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".