Access to Inclusive Education for Students with Autism: An Analysis of Canada’s Compliance with the United Nations Convention on the Rights of Persons with Disabilities
Bibliographic record
Abstract
This article argues that Canada fails to meet its obligation under article 24 of the United Nations Convention on the Rights of Persons with Disabilities to provide students with autism with access to inclusive education. Moving beyond Canadian legislation, under which every province and territory recognises the right of all students to an inclusive education, it analyses Canada’s education system and the implementation of the goal of inclusive education. It points out the effect of five interrelated factors on the inclusiveness of the Canadian education system and its accessibility for students with autism: reductions in funding for education; the inadequacy of individual support measures and parent participation; the lack of education and training for teachers; the use of language indicative of the medical model of disability by governments; and "voluntary segregation" – the voluntary removal of children from the public education system by their parents. It concludes that Canada likely does not meet its obligations under the United Nations Convention on the Rights of Persons with Disabilities.
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 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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.011 |
| Science and technology studies | 0.011 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".