How to Develop Intelligence Gathering in Efficient and Practical Anti-Doping Activities
Bibliographic record
Abstract
Prior to the formation of the World Anti-Doping Agency (WADA), the fight against doping in sport was not unified; instead, it relied on individual approaches established by various stakeholders to make it effective. The scandal of the Festina Affair, during the Tour de France 1998, and other drug doping scandals revealed the ineffectiveness and inadequacy of such an approach. The resulting media scandal raised public authorities' awareness about the necessity to deal with doping in sport with a harmonized and a more effective approach. The International Olympic Committee interceded and convened a World Conference on Doping, bringing together all parties involved in the fight against doping. As a result, WADA was established on November 10, 1999, in Lausanne to promote and coordinate the fight against doping in sport internationally. In this regard, the World Anti-Doping Code (WADC or the Code) is the core document harmonizing anti-doping rules and regulations within sport organizations and public authorities. The Code was instrumental in introducing the concept of "nonanalytical" rule violations, which are emphasized within the revised 2015 Code. Nonanalytical rule violations allow anti-doping organizations (ADOs) to apply sanctions in cases where there is no positive doping sample, but where there may still be evidence that a doping violation has occurred. This recognition of "nonanalytical" rule violations by WADA is the concrete result of taking into account lessons learned from prior infamous doping scandals. Thus, intelligence gathering, particularly through cooperation with global law enforcement agencies, is a key tool in the fight against doping. The 2015 Code and the international standards on testing and investigations establish and implement intelligence gathering as part of ADOs' routine activities in the fight against doping in sport.
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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 | 0.028 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.022 | 0.029 |
| Open science | 0.005 | 0.015 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.023 |
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".