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Record W4410778929 · doi:10.1111/socf.13059

Muslim women's agency; getting past the binary trap

2025· article· en· W4410778929 on OpenAlexafffund
Fauzia Husain

Bibliographic record

VenueSociological Forum · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicIslamic Studies and History
Canadian institutionsQueen's University
FundersUniversity of TorontoUniversity of VirginiaAmerican Association of University WomenNational Science Foundation
KeywordsAgency (philosophy)SociologyGender studiesTrap (plumbing)Media studiesSocial scienceGeography

Abstract

fetched live from OpenAlex

Abstract Although feminist theorizing on Muslim women's agency has come a long way, recent models reflect a one‐dimensional conception of agency that reinvigorates problematic binaries and undermines feminist politics. To address these limitations, the author focuses on the interpersonal arena where agency involves not only doing but also the cultivation and consolidation of relationships. Ethnographic data reveal three key findings: (1) Agency is multidimensional, encompassing both display work and the recruitment of others. (2) When we foreground agency's relational dimensions, binary conceptions fall apart—forms of agency that appear to involve compliance with dominant norms are revealed as also being resistant to an alternate set of norms favored by others. (3) Relational aspects of agency are socially patterned by social structures, such as class, that restrict marginalized actors from activating relationships in service to extension, a capacity to extend the locus of action over time and space. By examining these diverse dimensions of agency, the article underscores the connection between agency, exclusion, and inequality. This analysis not only challenges restrictive binaries within feminist thought but also opens up possibilities for feminist intervention.

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 imitation

Not 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.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.019

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0100.024
Scholarly communication0.0050.004
Open science0.0000.008
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.025
GPT teacher head0.301
Teacher spread0.276 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2025
Admission routes2
Has abstractyes

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