What Would Kim Do: A Choice Study of Projected Athlete Doping Considerations
Bibliographic record
Abstract
This paper reports on an empirical discrete choice model of the factors influencing a hypothetical athlete’s deliberations around using prohibited performance enhancing substances (doping) developed from a sample of 259 elite Australian athletes (76% Australian, Worlds or Olympic). Kim was constructed as a gender neutral athlete at the same level and stage of career as the respondent. The results indicate athletes felt Kim would be more at risk of considering doping if convinced by a coach or senior athlete of disproportionate immediate gains to performance with little or no consequences (e.g., low risk of prosecution). Conversely, athletes indicated Kim was felt to be less inclined to consider doping if doping would be fatal, to achieve or maintain performance, large fines ($150,000) or no financial gain. The choice model also indicates elite athletes’ projections about doping considerations were rational in character. The implications for managing the role of drugs in sport suggest antidoping could be improved with precisely timed testing, changing incentive structures within sport, concealing test accuracy and publicly humiliating athletes caught doping.
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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 | 0.013 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".