The development of doping use in high‐level cycling: From team‐organized doping to advances in the fight against doping
Bibliographic record
Abstract
In 1998, the Festina scandal at the Tour de France provided the first proof of widespread doping in professional cycling. This doping scandal marked the end of team-organized doping in professional cycling and ushered in a new period marked by the increasing implementation of anti-doping measures. This article evaluates the impact of the anti-doping rules and tests instituted since the Festina scandal. We adopt a psychosocial approach to analyze the organization of doping and the development of doping attitudes and practices in high-level cycling. Sixteen cyclists were interviewed, of which eight were young, current cyclists and eight were former cyclists who became professionals before the Festina scandal. Our results show that although the fight against doping in the last decade has reduced doping use in high-level cycling, anti-doping measures have also had unexpected effects. The fight against doping in cycling is not over.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".