Sex Segregation In Strength Sports: Do Equal-sized Muscles Express The Same Levels Of Strength?
Bibliographic record
Abstract
Concerns have been raised against the current two-sex binary category in sports competitions. It was suggested that if males and females were separated based on muscle size, it would negate the strength advantage between the sexes. Although statistically “controlling for” muscle size to predict muscle strength between sexes may provide some insights, this approach may be limited by the fact that there is minimal overlap in muscle size between sexes. PURPOSE: We tested possible sex differences in various strength outcomes when pair-matched for muscle thickness. We also compared the competition performance of the smallest male weight class in the adult division of the International Powerlifting Federation (IPF) to different weight classes in the female divisions. METHODS: Sixteen different data sets (n = 963) were assessed to pair-match females with males who had a muscle thickness value within 2%. 92 pairs were formed. Strength measurements included isotonic, isokinetic, and isometric tests. A comparison of the rank scores was provided to illustrate how often males outperformed females (or vice versa) with equal-sized muscle thickness. We also compared the top five highest lifted weights in IPF World Open Classic Championship (2015-2019) for the smallest male class (59 kg) to different weight classes in females (up to 84 kg). RESULTS: Males had greater strength than females in 87% of the observations in isotonic strength (54 wins by men, 4 by females, 3 ties). The magnitude of difference was d = 1.04; 95% CI 0.72, 1.3. For isokinetic strength, males had greater strength in 76% of the observations (35 wins by men, 11 by females, 0 ties). The magnitude of difference was d = 0.54; 95% CI 0.23, 0.85. For isometric strength, 88% of the observations were greater in males (30 wins by men, 4 by females, 0 ties). The magnitude of difference was d = 0.5; 95% CI 0.14, 0.86. Additionally, males in the lightest weight division in the IPF largely outperformed females in heavier weight divisions. CONCLUSION: Segregation based on surrogates of muscle mass might not be an appropriate classification to create fair competition within strength sports. This is not to refute the concept of the desegregation of the two-sex binary category but to present data that raise important concerns about the potential sex-based differences in strength performance.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".