Why don’t you Dope? A preliminary analysis of the factors which influence athletes decision not to Dope in Sport
Bibliographic record
Abstract
The purpose of this paper is to examine why athletes do not dope in sport. The research treats the ‘problem’ of doping as an issue of ‘control’ and draws on control theory (Hopwood, 1974; Byers, 2013) to analyze athletes choices not to engage in doping. Semi-structured interviews were conducted with cur- rent Canadian athletes, former athletes, coaches, and officials from seven different sports that competed in the CIS (Canadian Interuniversity Sport), national and international events, professional sport, Pan American Games, provincial teams, and World University Games. In total, 20 interviews were conducted with 7 female and 13 male participants. Results indicate that an over- abundance of administrative formal control mechanisms may be creating confusion and inefficiency in the doping control system. More powerful control mechanisms such as social and self-controls seem to be operating amongst athletes and issues of trust and the role of emotion are significant concepts that re- quire further research in this context.
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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 | 0.008 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".