Bibliographic record
Abstract
Adverse effects discrimination arises when a law that appears to be neutral on its face has a disproportionate and negative impact on members of a group identified by a protected ground.1 The discrimination is usually not as easy to see as it is in cases of direct discrimination, where distinctions are drawn by a law, program, or policy. This may be why Fraser v Canada (Attorney General)2 is only the third adverse effects claim under section 15(1) of the Canadian Charter of Rights and Freedoms3 to succeed since section 15 came into force in 1985.4 Fraser is notable simply because it is the first successful adverse effects claim in twenty-two years.5 1 Jonnette Watson Hamilton & Jennifer Koshan. “Adverse Impact: The Supreme Court’s Approach to Adverse Effects Discrimination under Section 15 of the Charter” (2015) 19:2 Rev Const Stud Studies 191 at 196 [“Adverse Impact”]. 2 2020 SCC 28 [Fraser]. 3 Part I of the Constitution Act, 1982, being Schedule B to the Canada Act 1982 (UK), 1982, c 11 [Charter]. 4 The other two cases in which adverse effects claims were successful were Eldridge v British Columbia, [1997] 3 SCR 624, 151 DLR (4th) 577 [Eldridge cited to SCR] and Vriend v Alberta, [1998] 1 SCR 493, 156 DLR (4th) 385 [Vriend cited to SCR]. 5 At least five adverse effects claims made under section 15 of the Charter failed in the intervening twentytwo years: Health Services and Support — Facilities Subsector Bargaining Assn v British Columbia, 2007 SCC
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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 | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.011 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.037 | 0.034 |
| 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".