Bibliographic record
Abstract
“The crux of a substantive equality analysis,” Professor Donna Greschner once wrote, “is critical scrutiny of the criteria that policy-makers use to differentiate.” This paper is in full agreement with Greschner’s point and, up until Kapp, it appeared as though the Supreme Court of Canada was as well. Part I of this paper explains that the Court’s initial endorsement of substantive equality reflected a fundamental understanding that inequality cannot always be seen at the surface. Often inequality must be uncovered, meaning “[w]e cannot assess whether a policy promotes or impedes substantive equality without examining people’s circumstances … independently of the words of the law itself.” With Part I having established the centrality of impact to the Court’s vision of substantive equality, Part II shows how the Court in Kapp, fearful of discouraging governments from ameliorating disadvantage, opted in favour of deference. The Court was willing to make sure that a law was genuine in its ameliorative intent; however, it was not prepared to force governments to prove (or disprove) the law’s precise impact. Part III, accordingly, seeks a middle ground. It agrees that section 15(2) of the Charter operates best in a threshold capacity, but argues that it is possible to insert scrutiny into the test without significantly enhancing the somewhat theoretical risk of deterrence.
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 | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.019 | 0.094 |
| Scholarly communication | 0.027 | 0.025 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.013 | 0.018 |
| Insufficient payload (model declined to judge) | 0.005 | 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".