Paper vs. Pixel: Can We Use a Pen-and-Paper Method to Measure Athletes' Implicit Doping Attitude?
Bibliographic record
Abstract
Doping attitude is an individual's subjective evaluation (e.g., good or bad, useful or useless) toward the use of prohibited performance-enhancing substances or methods in sports. Research on doping attitude has traditionally relied on self-report questionnaire methods to measure the construct (Ntoumanis et al., 2014; Chan et al., 2015). However, as doping in sport is illegal (World Anti-Doping Agency, 2015) and perceived as socially unacceptable, athletes who hold positive attitudes toward doping are less likely to reveal them to others. As a result explicit measures of doping attitude are susceptible to potential bias as athletes may respond in a socially desirable fashion (Petróczi and Aidman, 2009; Gucciardi et al., 2010). To counter such bias, implicit measures such as the implicit association test (IAT; Greenwald et al., 1998) have been developed to capture individuals' non-conscious attitudes toward doping (Brand et al., 2014a,b; Schindler et al., 2015). The current paper aims to introduce a paper-and-pen IAT which could potentially serve as alternative method to the traditional computer-IAT for measuring athletes' doping attitude.
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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 | 0.012 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".