Would they dope? Revisiting the Goldman dilemma
Bibliographic record
Abstract
BACKGROUND/AIM: Discussions of doping often report Goldman's sensational results that half of the elite athletes asked would take a drug that guaranteed sporting success which would also result in their death in 5 years' time. There has never been any effort to assess the properties of the 'Goldman dilemma' or replicate the results in the post World Anti-Doping Agency context. This research evaluated the dilemma with contemporary elite athletes. METHODS: Participants at an elite-level track and field meet in North America were segregated into an interview or online response. After basic demographics, participants were presented with three variant 'Goldman' dilemmas counter-balanced for presentation order. RESULTS: Only 2 out of 212 samples (119 men, 93 women, mean age 20.89) reported that they would take the Faustian bargain offered by the original Goldman dilemma. However, if there were no consequences to the (illegal) drug use, then 25/212 indicated that they would take the substance (no death condition). Legality also changes the acceptance rate to 13/212 even with death as a consequence. Regression modelling showed that no other variable was significant (gender, competitive level, type of sport) and there was no statistical difference between the interview and online collection method. CONCLUSIONS: Goldman's results do not match our sample. A subset of athletes is willing to dope and another subset is willing to sacrifice their life to achieve success, although to a much lesser degree than that observed by Goldman. A larger scale online survey is now viable to answer important questions such as variation across sports.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.018 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".