Children with autism in a sport and physical activity context: a collaborative autoethnography by two parents outlining their experiences
Bibliographic record
Abstract
Sport and physical activity contexts are entrenched with ableist perspectives which view disability as abnormal or negative. Consequently, those who deviate from cultural norms may experience inequity, exclusion, stigmatisation, non-accidental violence and maltreatment. Despite the commitment to ensuring sport and physical activity is safe and inclusive through policies and programmes, more knowledge is needed about the welfare-related experiences of persons with a disability in sport and physical activity to better protect them. This research used collaborative autoethnography and Goffman’s theory of stigma to explore two mothers’ experiences in a sport and physical activity context, including what they saw, what they felt and what they perceived their children with a disability experienced. This research shows both mothers experienced stigma (e.g. enacted, courtesy, affiliate) due to their immersion and the actions of others in these contexts. Further, both mothers also perceived that their children with a disability experienced the same types of stigma in these contexts as well as the negative consequences related to this stigma (e.g. bullying, social isolation, exclusion, judgement, labelling, anxiety). These acts of stigmatisation positioned both them and their children as outsiders within the stories. This collaborative autoethnography highlights the lack of provisions for disabled children and their families in sport and physical activity contexts, and the persistence of ableist views.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.006 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".