Evaluation of the Education Plan and Activities of Swiss Sport Integrity. A report of a science-practice co-operation.
Bibliographic record
Abstract
Introduction The WADA requires the National Antidoping Organisations (NADOs) to annually evaluate its education plan and activities to accomplish the International Standard for Education of the WADA Code (2021), preferably done in cooperation with an independent external partner from the field of science and research. Therefore, Swiss Sport Integrity (SSI) and Institute of Sport Science (ISPW) entered into a cooperation agreement in 2020 for annual evaluating SSI’s education plan and activities from 2021 to 2024. This contribution aims to reporting the process of such a science-practice cooperation and identifying factors that are critical for effective cooperative evaluation. Methods The evaluation concept was developed on sound theoretical considerations following evaluation critieria guidelines (e.g., Sander, 2006). The evaluation focus of SSI’s education plan and activities covers three aspects: basic principles, long-term aims, and key areas for action according to WADA Code. For data gathering, a questionnaire to Swiss elite athletes (Swiss Olympic Card holder) and a questionnaire to their respective coaches as instruments were applied. The questionnaires were conducted during the year as part of the SSI education courses, which are mandatory for all Swiss elite athletes. Additionally, internal data from SSI and Swiss Olympic were gathered. The analysed data were merged and summarised in the annual management report for WADA. The process of cooperation was then reflected among the actors involved in the evaluation with identifying phases approached and assessing critical factors, both restricting and promoting, for effective cooperation. Results As large part of the results – the athletes’ responses – are confidential and limited to internal use of SSI and reporting to WADA, the presentation is limited to displaying the athletes’ and coaches’ questionnaires and pointing to the methodological challenges and limitations faced. The presentation’s focus is on reporting the cooperation process and determing factors. Results reveal that accepting and including external perspectives, understanding of the framework conditions, mutual trust and engagement, dedicated task specification and distrubition, knowledge and data sharing are perceived beneficial for co-operating. Discussion/Conclusion The discussion highlights the identified key points for effective processing in a science-practice co-cooperation and point to how to ensure this (good practice). Then transferability to other science-practice co-operations is briefly discussed. Concluding, the continuation and expansion of the existing cooperation with the entry into force of the new WADA Code 2027 will be briefly outlined. References WADA. (2021). World Anti-Doping Code 2021 (Art. 18 Education). Montreal: WADA. https://www.wada-ama.org Sanders, J. R. (Ed.). (2006). Handbuch der Evaluationsstandards: Die Standards des "Joint Committee on Standards for Educational Evaluation". VS Verlag für Sozialwissenschaften.
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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 | 0.035 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".