“(Self-)Surveillance, Anti-Doping, and Health in Non-Elite Road Running”
Bibliographic record
Abstract
This article explores disciplining effects of current anti-doping surveillance systems on the health consequences of non-elites’ daily behaviors and habits. As they are left out of direct anti-doping testing and enforcement, it is tempting to argue non-elites are unaffected by anti-doping efforts focused on the elite level of their sport. However, it is because they are not subject to anti-doping surveillance systems nor forced to comply with anti-doping regulations that non-elites are implicated within the wider arena of disciplinary power that envelops both elite and non-elite athletes and anti-doping agencies (Foucault 1979). Drawing on data from 28 interviews with non-elite runners I argue these runners do conform to the rules and norms of their sport as far as they understand them, but their knowledge of banned substances is inadequate and many non-elite runners have only a superficial and sometimes incorrect understanding of doping. Many view doping and its associated health risks as a problem only of elite running, as well as a problem limited to only a handful of widely publicized performance enhancing drugs or doping methods. As a result of these misunderstandings non-elite runners are vulnerable to negative health effects of over the counter (OTC) medications and nutritional supplements, which they view as “safe” and part of normal training as a result of the current elite surveillance model of anti-doping. The recent death of a non-elite marathon runner linked to use of the unregulated energy supplement DMAA demonstrates, questionable products are used by runners who may not be fully aware of the risks of use.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".