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Record W4401117319 · doi:10.1080/1369183x.2024.2384999

Double immobility: Syrian refugee women navigating the voluntary and forced marriage binary in Egypt

2024· article· en· W4401117319 on OpenAlexaff
Dina Taha

Bibliographic record

VenueJournal of Ethnic and Migration Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicMulticulturalism, Politics, Migration, Gender
Canadian institutionsYork University
Fundersnot available
KeywordsRefugeeForced migrationCoercion (linguistics)Forced marriageGender studiesSociologyInvisibilityCriminologyPolitical scienceLaw

Abstract

fetched live from OpenAlex

Research on why women, especially racialized women, stay in undesirable relationships is scarce and often misses how intersecting inequalities affect their decisions and the wider range of consent and coercion. Syrian refugee women in Egypt, grappling with not just unwanted marriages but also displacement and uprooting are a case in point. Their limited cultural, social, and legal mobility complicates their decision-making and limits their options. I describe this compounded precarity as ‘double immobility’: immobility within the marriage and immobility within the country. A secondary displacement where this time they become ‘displaced in place’ (Lubkemann, S. C. 2008. “Involuntary Immobility: On a Theoretical Invisibility in Forced Migration Studies.” Journal of Refugee Studies 21 (4): 454–475). Drawing on qualitative interviews with Syrian refugee women in Egypt in 2017, I employ an intersectional lens to highlight the social, legal, and migratory locations of displaced women that exacerbate their predicament, prompting a re-evaluation of the binary views of force/voluntary and coercion/consent in discussions about entering, remaining, and exiting marriage. The paper offers a novel framework for understanding the intersection of gendered displacement and marital dynamics and contributes to the broader discussion within the sociology of gender, displacement, and marriage.

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.668

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 head0.175
GPT teacher head0.472
Teacher spread0.297 · 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 designQualitative
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
Published2024
Admission routes1
Has abstractyes

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