From Moore to More: How the Social Model of Disability May Be Applied to Build More Inclusive and Accessible Education Systems
Bibliographic record
Abstract
This article uses an analysis of the Supreme Court of Canada’s decision in the Moore v British Columbia (Education) case as a means to reflect on the ways in which both the Court and education officials may continue to protect and advance access to education for students with disabilities. Part II briefly describes the social model of disability, which asserts that the ‘disabilities’ encountered by people with physical or intellectual impairments most often result from socially constructed barriers to inclusion. Part III then moves to a discussion of Moore, which is the most recent Supreme Court of Canada case dealing with equality claims made on behalf of students with disabilities. This decision demonstrates that, while the Court has adopted important aspects of the social model of disability, it has not yet embraced the type of systemic approach required to achieve full equality for disabled children seeking access to education. Part III thus also considers a number of ways in which education officials can more fully embrace a social model approach in order to build on the foundation established by the Court to establish more inclusive and accessible education systems.
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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 | 0.010 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.027 | 0.072 |
| Scholarly communication | 0.018 | 0.013 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.008 | 0.011 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".