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Record W4414984759 · doi:10.1177/215416472405900107

Trends in the Inclusive Classroom Placement of Students with Autism Spectrum Disorder: A Retrospective Study

2024· article· en· W4414984759 on OpenAlexaffabout
Alexandra Minuk, Jordan Shurr, Saad Chahine, Francine Berish

Bibliographic record

VenueEducation and training in autism and developmental disabilities · 2024
Typearticle
Languageen
FieldNeuroscience
TopicAutism Spectrum Disorder Research
Canadian institutionsQueen's University
Fundersnot available
KeywordsAutismAutism spectrum disorderStaffingSpecial educationInclusion (mineral)PopulationDescriptive statisticsCensus

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.564

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.026
GPT teacher head0.334
Teacher spread0.308 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2024
Admission routes2
Has abstractyes

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