Bibliographic record
Abstract
Last year, section 15(1) claimants found themselves in the shadow of our Charter's monument to equality.All of the equality claims were rejected in ways that served to expose the disorderly underside of our understanding of equality, providing a glimpse beyond the stirring language and neatly outlined tests.In 2002, push came to shove.Narrow conceptions of equality and institutional competence pushed, and equality claimants were shoved.Liberty and choice pushed out other contenders for the position of "values underlying human dignity," while government motives stole the limelight from the harms experienced by the claimant.Both of these trends appear at odds with past jurisprudence and conventional wisdom about the scope of the section 15(1) protection as interpreted by the Supreme Court. 1 * The author would like to thank Patrick Monahan for the invitation to contribute on this subject, Robert Wai for his careful reading and helpful comments, Lauren Temple for her excellent research assistance, and Bruce Ryder for many fruitful discussions about s. 15(1) in 2002, particularly around Gosselin.All errors are of course my own.1 "Harsh, perhaps even misguided" refers to Gosselin v. Quebec (Attorney General) 2002 SCC 84, at para.69 per McLachlin C.J. (referring to the reasonable welfare recipient's perception of the challenged legislation).The cases considered in this account are Gosselin, Nova Scotia (Attorney General) v. Walsh, 2002 SCC 83 and (to a lesser extent
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.016 |
| Scholarly communication | 0.018 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.010 | 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".