Examining the Inclusion of Students with Autism Spectrum Disorder in Ontario Public Schools
Bibliographic record
Abstract
In Ontario, inclusive education is the first consideration for the classroom placement of students with exceptionalities (Ontario Human Rights Commission, 2018). Inclusion in the regular classroom is believed to be particularly beneficial for students with autism spectrum disorder (ASD), a neurodevelopmental condition characterized by persistent difficulties with social communication and social interaction as well as restricted and repetitive behaviours, activities, and interests (American Psychological Association, 2013). In making decisions related to the location of instruction and support for students with ASD, Identification, Placement and Review Committees (IPRCs) are meant to consider student-specific factors, such as level of adaptive functioning (Ontario Ministry of Education, 2017). There is evidence to suggest, however, that these decisions are also influenced by other variables, such as students’ age (Sansosti & Sansosti, 2012), academic performance (Segall & Campbell, 2014), and geographic locale (Kurth, 2015). The purpose of this study was to examine the inclusive classroom placement of students with ASD across the province as well as to identify some of the significant variables that are associated with these placements. Using a retrospective research design, descriptive statistics and multivariate analysis of variance were used to characterize the classroom placement of students with ASD over a 12-year period. Additionally, correlations between the percentage of students with ASD in inclusive classroom placements, school board variables, and region-related variables of interest were calculated. The findings from this study indicate that there has been a notable increase in the percentage of students with ASD in inclusive classroom placements over time. While the correlation analysis involving school board variables reinforced the idea that inclusion is not necessarily associated with student learning (Kalambouka et al., 2007), several region-related variables were found to be associated with inclusive classroom placement at the elementary and secondary levels. Not only can these findings characterize patterns and trends in the classroom placement of elementary and secondary students with ASD, but also, they highlight the utility of a geospatial approach in identifying some of the variables that can be used to direct efforts in increasing inclusive education placement decisions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".