Protocoll on the implementation of the concept “Sports assistance for people with disabilities” (SpAss) in Bavaria
Bibliographic record
Abstract
Introduction To enable people with disabilities to participate in organized sports and to initiate a change towards a more inclusive society, the SpAss project was developed based on the PAPAI project (Saari & Skantz, 2017). In the 2023/24 pilot phase in Würzburg, 9 children with disabilities were accompanied by trained sports assistants during a maximum of 10 training sessions in sports clubs. 6 of them remained active after the end of the project. The main project started in October 2024 and lasts 18 months. Aims and Objectives A specially designed training course for voluntary sports assistants will be implemented into the educational program of the Bavarian Disabled Sports Association (BVS). Financing is regulated by the existing structures of the care system. The development of an app is a central component, with the goal to bring all parties involved (athletes, assistants, billing service providers) together and promote a more inclusive sports community. Methodology The app will be used to collect quantitative data on the success of the assisted participation, drop-out rates, reasons for dropping out and an accompanying self-assessment regarding the athletes' self-concept. Data on the success of the advertising concept, the app and sports assistant training will be collected via the sports assistants. Perspectives We expect the finished training concept, the app with integration of the survey methods and an advertising concept in summer 2025. Conclusions We hope that the presentation of our plans and interim results will lead to a fruitful discussion for the further development of the project. References Saari, A. & Skantz, H. (2017). The PAPAI-model: a promising tool to increase sports participation and physical activity levels of children and young people with disabilities. Vista-conference Toronto 2017.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.059 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.195 | 0.039 |
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".