Bibliographic record
Abstract
While the Supreme Court’s recent decision in Moore v. British Columbia was concerned with the accommodation of disability in the public school system, the authors argue that the spectre of private school funding looms over the judgment. Given the political consensus supporting public schools in Canada, several substantive and remedial elements of the Moore decision should be viewed in a cautionary light. A standard of accommodation that is rooted in comparison to the services provided by better-resourced and less diverse private schools may not be attainable by or appropriate for public schools, and should the refore be eschewed as a standard of accommodation in analyzing equal access to public school education. at the remedial stage, the awarding of compensation to Jeffrey Moore’s parents for a decade’s worth of private school tuition risks transforming human rights tribunals into purveyors of private school vouchers, an American education policy rightfully rejected by Canadian legislatures. Human rights remedies should be concerned with improving the universally accessible public school system for the benefit of all students with disabilities instead of siphoning scarce resources towards a select few students able to attend private schools. The authors argue that the Supreme Court of Canada was right to reject the Human Rights Tribunal’s systemic remedies which would have, inter alia, shifted responsibility for individual special education programs from the local school boards to the Ministry of Education by mandating greater centralization at the Ministry level of the design, delivery and oversight of individual students’ special education programs. This role is best undertaken at the local level by teachers and specialized board personnel who are in the unique position to know a student’s individual needs and abilities, and the circumstances and resources of the particular board. Concern is raised that Human Rights Tribunals can impose such broad systemic remedies affecting major public policy issues like education funding and centralization subject only to review on the standard of reasonableness.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.015 | 0.006 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.017 | 0.020 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".