Understanding and overcoming challenges for the inclusion of learners with autism spectrum disorder in South African mainstream classrooms
Bibliographic record
Abstract
Despite policies having been developed by various countries to manage and support inclusive education, the inclusion of learners with varied abilities remains a global crisis. This predicament infringes on the human rights of these children, who are unable to access quality education in mainstream schools. In South Africa, the education system has implemented Education White Paper 6 on inclusive education and the Policy on Screening, Identification, Assessment, and Support, which govern inclusive education execution as well as sustainability for all learners, including those with autism spectrum disorder (ASD). This paper argues that policy enactment requires holistic intervention strategies in light of the changing contextual scenarios. Piaget’s theory of cognitive development underpinned the investigation, which took a paradigmatic and interpretive stance in a multiple cross-case study design to scrutinise 15 teachers’ narratives relating to the inclusion of ASD learners. The findings revealed unclear statistics, an unclear conceptualisation of ASD learners in South Africa, inadequate resources, and unaligned teacher education perpetuating factors hindering the inclusion of ASD learners. The paper concludes with recommendations for future research informing teachers and policymakers on the contextual successful inclusion of ASD learners in South Africa.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.018 | 0.018 |
| Scholarly communication | 0.010 | 0.013 |
| Open science | 0.002 | 0.020 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".