Sports Corruption: The History and Challenges of Anti-Doping Regimes in the U.S. and Abroad
Bibliographic record
Abstract
The International Olympic Committee first began drug-testing in response to the death of Knud Enemark Jensen, a Danish cyclist who collapsed of heatstroke during the 1960 Olympics in Rome and whose autopsy suggested that amphetamines played a role in his death. A wide range of performance enhancing substances was formally banned, and doping tests were administered to preserve the integrity of competition and to protect the health of athletes. The IOC’s initial regime was neither systematic nor robust, lacking both methodologically and technologically. However, the gap between doping and monitoring gradually began to close as testing became more accurate and reliable, beginning with the IOC’s first testing procedure for anabolic steroids in 1976 and highlighted by the disqualification of Canadian Sprinter Ben Johnson for a positive anabolic steroid test after winning the 100m sprint at the 1988 Olympic games.\nThe IOC’s early anti-doping efforts achieved modest success, but illicit drug use rapidly pervaded other competitions, notably cycling and track and field. The 1998 Tour de France, ignominiously dubbed the “Tour du Dopage” by media sources, was ravaged by various doping scandals. Prior to the race, the nine-member Festina team was disqualified after customs officers discovered a large supply of doping products at the Belgian-French border, as well as a document outlining systematic drug programs for each of Festina’s riders. After retrospective drug tests, 92% of participants who were either tested or confessed to drug use, including 9 of the top 10 finishers, were found to have used Erythropoietin, an IOC-prohibited hormone that significantly boosts red blood cell production.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.013 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".