Cheater, cheater, pumpkin eater: the Dark Triad, attitudes towards doping, and cheating behaviour among athletes
Bibliographic record
Abstract
We examined the relationships between the Dark Triad personality traits (Machiavellianism, narcissism, and psychopathy), attitudes towards doping, and cheating behaviour among athletes. One-hundred and sixty-four athletes completed a completed a matrix solving task within a specific time limit. Participants were told they would receive a financial reward for the total number matrices they could solve, but only 13 of the 20 matrices were solvable. This provided the incentive and opportunity for the athletes to cheat. Following this, athletes completed two questionnaires, which assessed the Dark Triad and their attitudes towards doping. All three Dark Triad personality traits correlated positively with attitudes towards doping and cheating behaviour. Regression analyses revealed that psychopathy and narcissism positively predicted attitudes towards doping, and narcissism emerged as a positive predictor of cheating behaviour. Attitudes towards doping correlated positively with cheating behaviour. The Dark Triad appears to be important in relation to both attitudes towards doping and cheating behaviour among athletes. In addition, our findings illustrate that favourable attitudes towards doping are linked with actual cheating among athletes. National Anti-Doping Organizations, sports federations, and coaches could assess athletes' Dark Triad scores and attitudes towards doping in order to identify who may be more likely to cheat.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".