Considering Harm Reduction as the Future of Doping Control Policy in International Sport
Bibliographic record
Abstract
Since the 1960s, major international sporting organizations enforced a prohibition on performance-enhancing drugs. The scope of this enforcement expanded to the current system regulated by the World Anti-Doping Agency. Although the sophistication of the detective sciences and the comprehensive enforcement of these prohibitions have improved over time, supporters of the ban on drug use in sport still struggle with 3 issues: Doping is still quite common; the ability to detect established drugs have driven users to newer, more experimental substances; and the prohibition policy lacks sufficient moral justifications. This article suggests that the debate over doping has bifurcated between those who continue to support antidoping measures with insufficient ethical grounds and those who would potentially permit the unregulated use of performance-enhancement technologies in sport because of insufficient justifications for prohibition. A third way, posed herein, suggests the most ethically defensible policy is a harm-reduction approach.
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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 | 0.027 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.041 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.027 | 0.013 |
| Insufficient payload (model declined to judge) | 0.008 | 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".