WADA Prohibited List: The Benefits of Combining Pharmacology, Medicine, and Law
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it