Doping Sanctions in Sport: Knowledge and Perception of (Legal) Consequences of Doping—An Explorative Study in Austria
Bibliographic record
Abstract
Anti-doping rule violations (ADRVs) can lead to sports-related and legal sanctions, thus, being knowledgeable is important. Research into this knowledge and how athletes and their support personnel (ASP) perceive the control mechanisms and the appropriateness of (legal) sanctions is still scarce. This explorative study aimed to examine the knowledge and perception of existing (legal) sanctions in Austria, by distributing a questionnaire to Austrian athletes and ASP covering the topics of knowledge related to legal and sports-related consequences associated with a specific ADRV presented in a case study, their trust and satisfaction with specific agencies (based on the European Social Survey (ESS)) and perceived efficiency and effectiveness of the doping control system. Data were analyzed descriptively. All respondents (N = 59) agreed on a ban from sport to be appropriate. Knowledge about legal consequences and the trust in the judiciary and the sport governing bodies was moderate (6.82 out of 10). Perceived appropriate consequences were on average higher than the likely sanctions to be faced. Future prevention should include trust building measures in the institutions and the control system, improvement in terms of access to law and education for the target group and critical reflection on the existence of social norms. Furthermore, the implementation of risk management aspects should be part of future approaches.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".