The Effect of Record Versus Rank Competition on the Performance of Male Marathoners
Bibliographic record
Abstract
We examined the performance differences in elite male marathoners when competing for record times versus ranks.Data of the top 300 male marathoners in 2019 were obtained from the World Athletics website for comparison and analysis.All competitions approved by the World Athletics were rated in the order of OW, GL, A, B, C, D, E, and F. Time comparisons were performed using one-way ANOVA and then the Bonferroni post-hoc test.Higher-grade competitions consist of top athletes with competitive qualifying record whose central motivation is to achieve the best records.Lower-grade competitions are often preliminary measures of qualification for larger competitions, motivating athletes to compete for ranks rather than records.The average time difference for each competition was statistically significant.GL's average time was the fastest at 2:13:42 (±00:03:15).From A to F, the average finishing time tended to increase from 2:09:51 (±00:03:27) to 2:14:48 (±00:03:24).The average end time at F was the slowest at 2:14:48 (±00:03:24).When comparing the athletes' relative performance, the times for large international competitions, such as GL, A, and B, were also faster than smaller competitions, such as E and F (p<0.05).These results are interpreted to mean that competing to achieve record times is better for marathon performance than competing for ranks.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".