Performance enhancement, elite athletes and anti doping governance: comparing human guinea pigs in pharmaceutical research and professional sports
Bibliographic record
Abstract
In light of the World Anti Doping Agency's 2013 Code Revision process, we critically explore the applicability of two of three criteria used to determine whether a method or substance should be considered for their Prohibited List, namely its (potential) performance enhancing effects and its (potential) risk to the health of the athlete. To do so, we compare two communities of human guinea pigs: (i) individuals who make a living out of serial participation in Phase 1 pharmacology trials; and (ii) elite athletes who engage in what is effectively 'unregulated clinical research' by using untested prohibited or non-prohibited performance enhancing substances and methods, alone or in combination. Our comparison sheds light on norms of research ethics that these practices exacerbate with respect to the concepts of multiplicity, visibility, and consistency. We argue for the need to establish a proper governance framework to increase the accountability of these unregulated research practices in order to protect the human guinea pigs in elite sports contexts, and to establish reasonable grounds for the performance enhancing effects, and the risks to the health of the athlete, of the methods and substances that might justify their inclusion on the Prohibited List.
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.
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 | 0.087 | 0.098 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.037 |
| Scholarly communication | 0.009 | 0.006 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".