Trends in the Inclusive Classroom Placement of Students with Autism Spectrum Disorder: A Retrospective Study
Bibliographic record
Abstract
Students with autism spectrum disorder have been shown to benefit both academically and socially from inclusion in general education classrooms, but concerns remain about the persistence of separate special education settings around the world. Using special education program placement data from the Canadian province of Ontario and census data summarized geospatially according to school board boundaries, the purpose of this study was to describe the trend in inclusive classroom placement for students with autism spectrum disorder over a 12-year period, as well as to establish any relation between staffing of educational assistants and population density with inclusive classroom placement. Descriptive analysis revealed a notable increase in the number of students with autism spectrum disorder in inclusive placements, and a corresponding decrease in the number of elementary students in specialized placements. Correlation analysis revealed an inverse relationship between staffing of educational assistants and population density with inclusive classroom placements at the secondary level, suggesting differences in the experience of inclusive education for older students. Interpretation of the findings underscore the importance of identifying variables associated with inclusive classroom placement, and the advantages of adopting a geospatial approach are highlighted.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".