Reframing Autism in a Mainstream Classroom via the Constructs of Inclusion and Stigma
Bibliographic record
Abstract
An investigation was undertaken to explore the social environment in a mainstream classroom that housed both non-autistic and autistic students. The research focus was to see if engaging students in a particular programme, successfully reduced stigma. The research is founded upon the concept of autism as part of neurodiversity rather than only disorder. It posits affective and social outcomes of inclusive education can be a reality once stigma is exposed and autism, reframed. The literature reviewed yielded valuable insights into how best to support the rationale of this study to prepare typical classmates for entry of autistic children. Method: Via a single case-study, an engaging and interactive Peer Preparation Programme (PPP) provided opportunities for the pupils and the focus child to interact with each other; enabling them to acquire knowledge about themselves, each other, autism and ways to minimise the negative impact of stigma. Results: Findings indicated a considerable increase in empathetic attitudes, spontaneous interactions, peer awareness, positive peer imitations and peer advocacy. Conclusions: To reframe autism within mainstream classrooms, although conscious this is a single case-study, the program helped pupils comprehend the sensitive issues of social stigma and encourage empathy in practice.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| 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".