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Record W2581531563 · doi:10.1353/ff.2016.0046

Discourses of Shari‘a Law and Muslim Women: A Critical Reflection on Sharia in Canada

2016· article· en· W2581531563 on OpenAlexaboutno aff
Tabassum Fahim Ruby

Bibliographic record

VenueFeminist formations · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsnot available
Fundersnot available
KeywordsShariaMulticulturalismFaithIslamArbitrationLawNarrativeSociologyGender studiesOrientalismAdulteryPoliticsColonialismEconomic JusticePolitical scienceHistoryTheologyLiterature

Abstract

fetched live from OpenAlex

In 2005, the National Film Board of Canada released the documentary Sharia in Canada , which had been produced in the midst of the massive legal and public discussions following Ontario’s Islamic Institute of Civil Justice’s announcement that, under the Ontario Arbitration Act, Muslims could resolve their family disputes through faith-based arbitration. The documentary offers a remarkable case study in the intersections of imperialist and sexist discourses, in its characterizations of arranged marriages, intolerance of homosexuality, lapidation for adultery, domestic violence, and the practice of the hijab to the identity politics of multiculturalism. This article unpacks these intersections by focusing on critics of faith-based arbitration as featured in the documentary—critics who overwhelmingly self-identified as Muslims or as descendents of Muslims. Their arguments divide women into two categories: enlightened subjects, who can make informed decisions, and oppressed subjects, who need to be rescued. While such representations feed into longstanding Orientalist discourses, they also indicate a shift in those same tropes —for now it is brown women and brown men who shall save brown women from brown men. From this perspective, the article underscores the ways insiders participate in the colonial narrative and afford a powerful voice to Islamophobic and civilizational projects.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.901
Threshold uncertainty score0.504

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.024
GPT teacher head0.309
Teacher spread0.285 · 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 designTheoretical or conceptual
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

Citations2
Published2016
Admission routes1
Has abstractyes

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