interaction between age and gender in ultramarathon performance times
Bibliographic record
Abstract
The predictors of ultramarathon performance remain uncertain. Although men tend to have faster finishing times, low entrance rates for women and historical rules banning women from long endurance events suggest social barriers might play a role. The objective of our study was to examine, using data from the Comrades ultramarathon how the gender gap for finishing times changed longitudinally in the various age groups. We hypothesized that this gap would show both a historical decrease, and also be less in older participants. The Comrades data set has the declared gender, age category, running time, year of the event and the direction of the event (up versus down) for each participant. The age categories are Senior (20 to 39 years old), Veteran (40 to 49 years old), Master (50 to 59 years old), and Grandmaster (age greater than 60 years old). The performance gap between women and men was less in the older as compared to the younger age groups (F = 76.51, p < .001). This difference in finishing times between men and women became less over time in our longitudinal analysis (β = -0.377 ± 0.158, p = .021) and was quite small (12 minutes) in the Grandmaster age category.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".