From Equal Access to Individual Exit: The Invisibility of Systemic Discrimination in Moore
Bibliographic record
Abstract
This article explores the implications of the Supreme Court of Canada decision in R. v. Moore, 2012 SCC 61, wherein the Court narrowed a systemic discrimination claim for equal access to public education by children with learning disabilities, to an individual claim by one learning disabled child alone. In Moore, the Supreme Court upheld a finding of discrimination against a local District School Board in the Province of British Columbia for failing to provide the claimant, Jeffrey Moore, with the learning supports he required in the public school system. The SCC, however, dismissed the claim against the Province and struck the systemic remedies, which included requiring the Province and District to monitor and assess special education services and to ensure that all school districts have a range of services in place to meet the needs of severely learning disabled students. The only remedy upheld by the SCC was compensation to the Moores, including the costs associated with private school tuition for their son.The case comment traces the narrowing of the case from a systemic claim at the trial level to an individual claim at the SCC. The factual context of the continued absence of monitoring of, and standards for assessing, special education services is discussed. The lack of system-wide standards and monitoring remains a critical impediment to equitable access to education by children with special needs. The substantive and strategic implications of the SCC’s individualized analysis in Moore for future human rights claims which challenge patterns of discrimination in public service delivery are explored. The individualized analysis and remedy risk promoting publicly-funded exit from the public school system rather than fulfilling the statutory goals of the Human Rights Act and Schools Act of inclusivity and equity.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.046 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.011 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 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".