Classification of middle- and long-distance runners based upon their performance
Bibliographic record
Abstract
The scientific literature lacks consensus on classification of middle- and long-distance runners. This creates situations where the sample studied may not represent the target population and could produce misleading conclusions. Thus, we present an approach for a data-driven classification of middle- and long-distance runners according to their competition results. The best annual results of middle- and long-distance track runners participating at major (Olympics, World and European Championships) and national championships (Denmark, Sweden, Finland, Norway) were gathered for the 2012-2018 period. Overall, 1920 men's and 1808 women's performance results were gathered. The results were grouped accordingly. Quadratic discriminant analysis was applied to define the limits between the groups. Three basic categories could be proposed for classification: world class, international and national. Classification provides a realistic overview of performance standards and the number of athletes for different categories in middle- and long-distance track running in real-world settings. The performance-based classification provides data-driven and unified criteria for reporting standards on athletes' proficiency levels. It allows for more consistent reporting practices on the target population in research. In addition to scientific research, the classification could also be employed for a variety of practical purposes.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".