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Record W2168600337 · doi:10.1177/0002764210368096

In the Name of Equality? The Missing Intersection in Canadian Feminists’ Legal Mobilization Against Multiculturalism

2010· article· en· W2168600337 on OpenAlexaffabout
Éléonore Lépinard

Bibliographic record

VenueAmerican Behavioral Scientist · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicMulticultural Socio-Legal Studies
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsMulticulturalismPolitical scienceIntersectionalityLawGender studiesArbitrationContext (archaeology)Ethnic groupEconomic JusticeHarassmentSociologyGeography

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.331
Threshold uncertainty score0.933

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.034
GPT teacher head0.371
Teacher spread0.337 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations12
Published2010
Admission routes2
Has abstractyes

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