Do Gender Differences in Running Performance Disappear With Distance?
Bibliographic record
Abstract
It has been suggested that gender differences in running should disappear as distances increase, particularly past the marathon. This suggestion is primarily based on differences in fuel utilization, muscle damage following exercise, relative improvements in performance over the past decades, and on the analysis of marathon vs. ultramarathon performances of men and women. We reasoned that the best comparison of the potential of a human is by the use of world best times, which should be reasonable indicators of the effect of distance on relative performance of women and men. We compared current world best running performances at distances from 100 m to 200 km. Records as of December 2002 were obtained. T-tests analyzed speed differences between genders, and regression analysis tested the percent differences between men and women across distance. Speeds were different, with the average difference being 12.4% faster for men. There was a significant slope to the speed difference across distances in that longer distances were associated with greater differences. These results may be confounded by the reduced number of women in longer distance events. Furthermore, the proposed metabolic advantage for women because of increased fat metabolism may be masked by regular feeding during endurance races.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".