Governance, harmonisation, & genetics: The world anti‐doping agency & its European connections
Bibliographic record
Abstract
This paper documents the political positioning of Europe in relation to anti‐doping initiatives generally and the World Anti‐Doping Agency (WADA) specifically to better understand the status of anti‐doping in world sport and the possibility for managing the so‐called drug war in sport. The paper begins by detailing the historical context of WADA and its relationship to the EU. In so doing, it is revealed that the interest in contemporary anti‐doping discourse for harmonisation is made problematic by enduring differences between the two organisations. The EU has withdrawn its funding of WADA on the basis of unsatisfactory conditions relating to budget transparency and the political position of WADA One of the major points of contention is the question about WADA's independence from the International Olympic Committee (IOC). Despite having moved from Lausanne (Switzerland) to Montreal (Canada), WADA remains embroiled in IOC structures, continuing with 50% of its budget coming from the IOC. As well, WADA is chaired by controversial IOC figurehead Richard Pound, whose background is very much embroiled in Olympic politics. With some uncertainty about the depth of IOC reforms, coupled with Richard Pound not having attained the IOC Presidency, and Canada not receiving the 2008 Olympic Summer Games, the political context of WADA remains uncertain and to the dissatisfaction of the EU. This is fundamentally problematic from the perspective of harmonising anti‐doping policy and from the general efforts of anti‐doping policy makers. A further basis for arguing why these circumstances are unsatisfactory for the EU is recognised by considering emerging methods of doping, specifically genetic manipulation. It is argued that the EU and governmental organisations in general cannot hold fast to the approaches of anti‐doping agencies such as WADA This is because their approaches to solving the drug problem in sport are not premised upon the protection of individuals, which are guiding principles in the EU and other governmental organisations. WADA's commitment to prioritising the values of fair play and other sporting values does not fit with the way in which non‐sporting drug policies are formulated which, instead, focus upon the broader medical priorities implicated by drugs. As new kinds of doping emerge in genetics, the role of sports ethics is less of a priority and this must be built into the way in which anti‐doping is approached by WADA For this reason, it is concluded that the EU and other inter-governmental organisations, much more than WADA, are better placed to respond to the greater need for harmonising policies and for addressing emerging technologies. While it is appealing to have a world antl-doping organisation, Its success relies heavily upon the unity of nations, governmental management, broader biomedical ethical considerations, and the ability to remove sport from the equation. On this level, harmonisation responds to much more than to antl-doping policy. It involves a harmonisation of broader medical policies in relation to drugs, genetics, and the policing of them. However, there is a danger that ethical discussions will still be subservient to policy decisions, where the ethical conclusions have already been made.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.015 |
| Scholarly communication | 0.014 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".