Game changers: school sport as a resource of hope for students with disabilities
Bibliographic record
Abstract
This research focuses on education’s transformative potential, particularly as it relates to school sport for students with disabilities. The study introduced a participatory action research approach, using Game Changers – an inclusive sport program co-designed with students with disabilities using a human-centred design approach to address the historical underservicing of students with disabilities in school sport – as an intervention. Students with disabilities, their schools’ physical education and learning support teachers, community partners, and university researchers came together in this research, hoping to improve sport-related opportunities and outcomes. Game Changers, as a resource of hope, provided a model that worked well to serve the needs of individuals and communities traditionally marginalized or oppressed. Data collection methods included pre- and post-program surveys and focus group interviews. Results revealed that Game Changers not only impacted individuals’ lives, as students with disabilities reported higher levels of perceived competence and autonomy, but also symbolized a commitment to social change within school communities. The program’s success has led to its continuation in other schools and the development of a guidebook for implementing similar programs in school communities. The study concludes that Game Changers contributes to social change and, as a resource of hope, serves as a model for inclusive sport programs within schools.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".