In the Name of Equality? The Missing Intersection in Canadian Feminists’ Legal Mobilization Against Multiculturalism
Bibliographic record
Abstract
In Canada, women’s rights organizations have successfully mobilized the law to foster gender equality. In doing so, they have been constrained by legal understandings of equality and discrimination, which have shaped their strategies to seek justice. In return, their mobilization, mainly through litigation, has contributed to craft or to alter legal categories (such as “substantive equality,” “women,” “sexual harassment,” etc.), which in turn sustain their identities and their interests. However, claims made in the name of gender equality raise two issues: They tend to overlook the intersection of gender with other grounds of discrimination such as religion or race/ethnicity; and they tend to conflict with multiculturalism, a value enshrined in Canadian law. The recent decision taken by the province of Ontario to ban religious arbitration for family matters offers an illuminating case study of this tension between gender equality and religious rights in the Canadian context. This article analyzes women’s rights activists’ legal understandings of gender equality and religious/ethnic discrimination to explain how these representations have influenced women’s mobilization against religious arbitration in Ontario. Bringing together the insights developed by critical legal studies about intersectionality and the study of legal mobilization, this articles explores through a concrete example the tension between feminism and multiculturalism.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.071 | 0.061 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".