Breaking Down the Myth and Curse of Women Athletes: Enough is Enough, Period
Bibliographic record
Abstract
From a theoretical perspective, I analyze the claim that women’s athletic performances are negatively affected by their menstrual cycles. To demonstrate the perpetuation of the belief that menstruation is a mythical debilitating bodily function for women and sport participation, an overview of Elizabeth Spelman, Simone De Beauvoir, and Iris Marion Young’s philosophical framing of somatophobia and menstruation is outlined. Analysis of specific examples of elite female athletes who have addressed menstruation in connection to their sporting performance are also discussed to emphasize how menstruation is linked to the frailty myth. I offer an analysis of the scientific literature on hormonal swings of the menstrual cycle and, the effects on sport performance to show that research is equivocal. Finally, a brief examination of feminine hygiene marking campaigns takes place to further emphasize the argument how the frailty myth is closely linked to women athletes and menstruation and how change can be created.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.040 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".