Should We Allow Performance-Enhancing Drugs in Sport? A Rebuttal to the Article by Savulescu and Colleagues
Bibliographic record
Abstract
Savulescu et al. [1] propose that, since it will never be possible to control drug use in sport, athletes should be allowed to use those performance-enhancing drugs that are “safe”. The authors fail to explain, however, why appropriate doping control has yet to be achieved in world sport. In this rebuttal, it is argued that the widespread doping of elite athletes, as is now common, cannot easily occur without government collusion that is either overt or covert. There is also evidence that a number of international sporting bodies have followed the same principle. Furthermore, since their products are so readily available to elite athletes, those pharmaceutical companies that manufacture the most popular performance-enhancing drugs would appear to be indifferent to the misuse of their products by athletes for nonmedical purposes. The control of drug use in sport has never been achieved, because these three stakeholders who should have acted to eliminate doping in sport appear to have chosen an opposite action without due consideration for their ethical responsibility to protect athletes from the proven dangers of doping. Doping in sport can only ever be defended for exclusively commercial reasons (both legal and criminal) and certainly not on the illusory ethical grounds proposed by Savulescu et al. [1].
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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 | 0.010 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.052 | 0.051 |
| Insufficient payload (model declined to judge) | 0.006 | 0.008 |
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".