The Emergence of Moral Technopreneurialism in Sport: Techniques in Anti-Doping Regulation, 1966–1976
Bibliographic record
Abstract
This article focuses on the early work of the International Olympic Committee (IOC) Medical Commission's anti-doping policies as a unique form of moral entrepreneurship. As the concept suggests, the Medical Commission's rule-making power relied, in part, on members' expertise and their status as elites. It also came to depend upon technopreneurialism, that is, entrepreneurial scientific innovation, particularly in relation to methods of detecting evidence of doping. Attending to these distinctions, this article argues that these early efforts reveal the emergence of ‘moral technopreneurialism’. By this, I refer to how technological developments serve and, in turn, shape anti-doping goals. Through an analysis of primary IOC documents and archival materials housed in Lausanne, Switzerland, this article considers how the Medical Commission implemented testing to detect evidence of doping from the mid-1960s through the 1976 Olympic Games in Montreal. These Games mark the introduction of anabolic steroids testing, which is noteworthy because the events leading up to it illustrate how policy-makers pushed for urgent scientific development, now an accepted trope in the fight against doping. This article concludes with a reflection on how technopreneurialism has culturally impacted the institutionalisation of the moral crusade 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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.012 | 0.086 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".