Fairness in Olympic sports: How can we control the increasing complexity of doping use in high performance sports?
Bibliographic record
Abstract
Fairness in Olympic sports: How can we control the increasing complexity of doping use in high performance sports?☆With the Rio Olympics just a few months past, we remember the excitement, the incredible performances, and the controversies.As happens so often, the Olympic Games focus attention not only on athletic achievements but also on how athletes may reach ever-increasing levels of performance.Doping, medical doping exemptions, and the ban of the Russian team for state-sponsored organized doping of its athletes dominated the discussions.After the Rio Olympics, it was revealed that many prominent athletes had health-related drug exemptions and thus were allowed to use medication containing substances banned by the World Anti-DopingAgency (WADA).Particularly, exemptions for asthma medication and attention deficit hyperactivity disorder came under scrutiny.The number of exemptions became suspicious when it was revealed that in certain sports (e.g., cycling and swimming), the percentage of athletes suffering from asthma is much greater than in the average population.Asthma medications, such as salbutamol and terbutaline, are said to be not performance enhancing, but they will increase performance in asthmatic athletes by up to 10%. 1 However, it is impossible to know what the normal performance level of an asthma sufferer would be were she or he free of asthma.This argument is reminiscent of the discussions surrounding Oscar Pistorius, the double-amputee "blade runner" who participated in the London Olympics, and scientists' disagreements about whether Mr. Pistorius had an unfair advantage running with his specialized lower limb prostheses.Of course, we will never know, because nobody can determine with certainty how fast Mr. Pistorius could have run the 400 m sprint had he had his "normal" legs.One might argue that when a drug contains banned substances, there should be no medical exemptions.Asthma sufferers should be allowed to use banned substances but should be categorized as Paralympic athletes and compete against each other in a separate competition.In swimming and cycling, approximately 40% of all Olympic athletes would fall into this category, making winning in the asthma category as competitive as the regular Olympic swimming and cycling competitions.Another drug that has received intense scrutiny and press is meldonium, which can be purchased under the trade name Mildronate.Meldonium was developed in the 1970s at the Latvian Institute of Organic Synthesis in the former Soviet Union.It is primarily used as a treatment against coronary artery diseases and has seen widespread use among Eastern European athletes.
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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 | 0.028 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.008 | 0.013 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".