Bibliographic record
Abstract
In Auton v. British Columbia, our courts faced a perfect storm created by colliding social, political, and legal forces. The end result of the litigation in Canada was a case described by the Supreme Court as “the first case of this type to reach this court.” in the view of the Supreme Court, the unanimous findings in the lower court had been built upon an incorrect premise that the provincial medicare scheme conferred a statutory right to public funding for all medically necessary services. The Court concluded that outside these core medical services, the statute had granted administrative discretion as to whether to extend public funding for treatments, such as intensive behavioural the rapy or other professional disciplines, such as behavioural the rapy. The Court went on to consider whether the petitioners were wrongly excluded from funding under the statute as properly construed. The perfect storm underlying Auton raises the more general question of the intersection of equality rights and the development and administration of social programs. In this paper, Geoffrey Cowper addresses some of the criticisms made of the result and reasoning in Auton. He the n addresses briefly the results and reasoning in other social benefit cases. That analysis suggests that the Court does not have a fixed approach to equality claims which arise in the context of social benefit programs. Rather, as in Auton, the Court appears consistently to prefer a more narrowly legal means of resolving the disputes rather than employing general questions of social policy and considering how equality analysis may facilitate or interfere with identified social objectives. To the extent that the decided cases indicate a trend, the Court appears to have little hesitation when it is convinced that the use of the distinction in its context is arbitrary and unfair.
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 | 0.010 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.031 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".