Examining the Current and Future Scientific Field of Antidoping: “Cheaters Should Never Win”
Bibliographic record
Abstract
To frame the current advances in anti-doping sciences, an initial definition of doping is necessary while it may in all cases foster a lively debate. The 2021 World Anti-Doping Code defines doping in Article 1 as "one or more of the anti-doping rule violations set forth in Articles 2.1 to 2.11 of the Code" (WADA, 2019) with an extremely detailed "Prohibited List" covering the Use or Attempted Use of doping substances and methods and certain malicious practices (Pavot, 2020). More simplistically, antidoping provisions may be considered violated when an athlete uses or attempts to use a prohibited substance or method or when a prohibited substance is detected in an urine or blood sample. Much then relies on the technical ability of an antidoping laboratory to detect such method or substance within a strict scope of international standards and operating guidelines. In an ideal scenario, laboratories would define and disseminate standard testing procedures for all kind of existing and upcoming substances, with unequivocal criteria for the definition of positivity, and the procedures would have been previously validated in blinded randomized and controlled studies with athletic subjects from both sex. Moreover, the epitome of experiments would make the sanctioning process swift with undeniable definitions of substances, dose and timing of use, administration, and individual metabolic variations
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".