Inclusive Schooling for Students with Disabilities: Redefining Dialogues of Diversity and Disability in the Canadian (Alberta) Agenda
Bibliographic record
Abstract
Inclusive education for students with disabilities is beset by foundational problems often related to conflicting definitions. UNESCO, a lead agency, speaks to accommodating diversity; a parallel conversation is preoccupied with disability. This paper is situated at the intersection of diversity, disability, and inclusive schooling. It focuses on the present tendency to conflate disability with diversity to conform with UNESCO’s version of inclusive schooling. As a case study, we use the Canadian province of Alberta where a recent set of proposals aimed at reforming special education rebranded disability as diversity and promised inclusive schooling as a solution to mounting diversity in the schools. We explicitly argue that Alberta’s sustained muddle of intent related to inclusive schooling arises, at least in part, from efforts to follow UNESCO’s broad prescriptions and assimilate disability into diversity. Misassumptions about the uniqueness of disability relative to other forms of diversity and difference have spilled over to blanket disability and diminish the importance of schooling for those disabled in the political space. Implicitly, the data are generalizable to other countries pursuing an inclusive agenda, particularly those in Europe.
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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.060 | 0.042 |
| Scholarly communication | 0.015 | 0.004 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".