Bibliographic record
Abstract
In this brief reflection, we look back on 32 years of equality advocacy and outcomes at the Supreme Court of Canada. We recount the fraught trajectory of the Supreme Court of Canada’s section 15 jurisprudence over the past decades, tracing the evolution of three distinct approaches to equality rights and highlighting the unique role that the Women’s Legal Education and Action Fund (LEAF) and its feminist equality advocacy has played in shaping the Court’s jurisprudence at each stage of section 15’s life. We then look to the future of section 15, suggesting that recent jurisprudence indicates the emergence of a new era in equality rights at the Supreme Court characterized by a distinct turning away from section 15 arguments. We offer some preliminary comments on what this trend might mean for the future of equality rights and for the future of feminist litigation strategies under the Charter, arguing that some of LEAF’s ideas about how to pursue substantive equality under section 15 that have not been acknowledged by the Court offer promising new directions for the next chapter of section 15 jurisprudence. We conclude that while under the current state of section 15 jurisprudence equality rights may be imperilled, the promise of section 15 remains.
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 | 0.007 | 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 | 0.018 | 0.071 |
| Scholarly communication | 0.019 | 0.013 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.007 | 0.010 |
| 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".