Nobody wants liars in sports?! Doping perception and ideas for prevention in a school setting
Bibliographic record
Abstract
Doping is one of the major crises in high-performance sports, which emphasizes the necessity of an efficient doping prevention. Ideas how to initiate doping prevention have been complemented by a broader and societal perspective (Petroczi, Norman, & Brueckner, 2017). In order to protect young athletes and to conduct constructive discussions without focusing on moral aspects or sanctions, alternate ideas (e.g., in Germany) aim to integrate doping and prevention in school settings and can be integrated in the concept of physical literacy (e.g., Lundvall, 2015; Ontario Curriculum). An online survey was conducted to gain a deeper understanding of doping-related prerequisites and to deduce recommendations how doping prevention could be integrated in physical education. To the time of submission, the sample (n = 51) is characterized by students (63%) who want to become teachers in physical education. In line with elite sport coaches (Poppel & Busch, 2019), participants perceive a higher severity of doping and doping prevalence in sports (international elite sports: M = 51.7%) than official data of the World Anti-Doping Agency (1.6%, 2017) indicate. From their point of view, doping(prevention) should be integrated in school subjects like physical education, biology or ethics. According to the ideas of literacy in health and physical education contents should be worked out in discussions, own little research projects, critical analyses, or role plays. The efficacy of transdisciplinary prevention projects needs to be proven in further research. It is planned to conduct further research in Canada as a reference sample.
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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 | 0.005 | 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 | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".