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 (Faiss et al., 2019).In the current world of global sports, the context is much more complex with each sporting performance scrutinized, and criticized often with a distorted judgement.The fight against doping is today at a crossroads.Multidisciplinary issues at stake in the social, biological, and global sciences should enable the system to move forward so that cheaters never win-or at the very least-their victory is increasingly difficult and risky through the threat of control, denunciation, or otherwise.In this context, the contribution of non-biological disciplines also appears fundamental throughout the process.Education is a critical issue since the culture of the fight against doping must be instilled in athletes and their entourage from an early age.Moreover, the sanction-based argument is now outdated and a new argument, based on the values of clean sport, must be supported.However, these programs are often implemented by National Antidoping Organizations (NADOs) who face multiple challenges in making them efficient (Gatterer et al., 2020).Other topics demonstrate the usefulness of the contribution of disciplines such as law, political science, communication, and even marketing.Obviously, with the importance of the World Anti-Doping Code and disputes before the Court of Arbitration for Sport, the importance of the legal field seems natural.Political and governance issues have received renewed interest in recent years: one can think, for example, of the concerns raised by the Office of National Drug Control Policy (ONDCP) Report of 17 June 2020 to the U.S. Congress regarding WADA Reform Efforts, which suggested, among other things, that the U.S. financial contribution to WADA be suspended.This report focuses-albeit in a biased way-on the issues at stake in the governance reform of WADA that is currently underway as well as on the major political issues that are currently at stake in the fight against doping.We could also talk about the relationship between WADA and the IOC, NADOs, and international federations, the question of gender representativeness in antidoping
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 | 0.011 | 0.041 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.010 | 0.009 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.024 | 0.030 |
| Insufficient payload (model declined to judge) | 0.011 | 0.009 |
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".