WADA Prohibited List: The Benefits of Combining Pharmacology, Medicine, and Law
Bibliographic record
Abstract
The yearning to win sports competitions has led some athletes to dope. Doping in sports is a real threat to the ‘Spirit of Sport’ and fairness. The pharmacokinetics of performance-enhancing drugs differ, as do their effects and purposes of use. As one of the most effective and decisive solutions, the idea to issue a prohibited list came to raise the legal awareness level among athletes about the types of prohibited substances and methods they have to avoid and in which time specifically. In addition, for the sake of broader and more comprehensive cooperation between the law, medicine, and pharmacology, to confront the phenomenon, and limit it to the narrowest possible scope on the other hand. The idea to issue the prohibited list came. Historical, descriptive, and legal approaches are employed in conducting this review. Additionally, the method of conceptual analysis is used to discover the exact normative terminology. The most significant finding for this review is that the issuance of the Prohibited List brought greater stability to sporting events. Its annual issuance is legal proof in front of everyone (countries, international sports organisations, and 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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.013 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| 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".