“(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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".