Feminist Equality Rights Litigation: Evolution of the Canadian Legal Landscape
Bibliographic record
Abstract
This publication was created as part of LEAF’s Feminist Strategic Litigation (FSL) Project. The FSL Project examines the use and impact of feminist strategic litigation to help LEAF, feminists, and gender equality advocates more effectively combat systemic discrimination and oppression. Recognizing that the legal fight for equality remains a work in progress, this report examines how the landscape of Canadian equality rights litigation has evolved since 1985. It looks both at how the legal meaning of equality has evolved and how feminists have developed distinct ways of working to advance equality. The report examines: (i) strengths and successes of feminist litigation; (ii) areas in which feminist litigation has not gained traction, has faced resistance or has encountered losses; (iii) areas which have yet to be explored or are under-developed and so present opportunities for future action; (iv) strategies that various legal and political actors have adopted to push back at feminist litigation; (v) changes in legal procedures that affect the availability or effectiveness of different litigation options; and (vi) the perpetual concern about resources. This report aims to provide a base of information and analysis from which LEAF and equality advocates can think critically and strategically about how to move forward.
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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 | 0.012 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.041 | 0.029 |
| Scholarly communication | 0.021 | 0.005 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.011 | 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".