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Sex Segregation In Strength Sports: Do Equal-sized Muscles Express The Same Levels Of Strength?

2023· article· en· W4387061896 on OpenAlexaff
Ryo Kataoka, Robert W. Spitz, Vickie Wong, Zachary W. Bell, Yujiro Yamada, Jun Song, William B. Hammert, Scott J. Dankel, Takashi Abe, Jeremy P. Loenneke

Bibliographic record

VenueMedicine & Science in Sports & Exercise · 2023
Typearticle
Languageen
FieldMedicine
TopicSports Performance and Training
Canadian institutionsMcGill University
Fundersnot available
KeywordsIsometric exerciseMuscle strengthIsotonicMathematicsBiologyMedicinePhysical therapyPhysical medicine and rehabilitation

Abstract

fetched live from OpenAlex

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.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.035
GPT teacher head0.312
Teacher spread0.277 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2023
Admission routes1
Has abstractyes

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