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Record W3020863605 · doi:10.1177/1350506820919146

‘After all, I have to show that I’m not different’: Muslim women’s psychological coping strategies with dichotomous and dichotomising stereotypes

2020· article· en· W3020863605 on OpenAlexaff
Katharina Hametner, Natalie Rodax, Katharina Steinicke, Jessica McQuarrie

Bibliographic record

VenueEuropean Journal of Women s Studies · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsCrandall University
Fundersnot available
KeywordsGender studiesNarrativeSociologyEthnic groupCoping (psychology)NegotiationSocial psychologyPsychologySocial sciencePsychotherapistAnthropology

Abstract

fetched live from OpenAlex

More than ever, ‘the headscarf’ is a dominant trope in contemporary ‘Western’ discourses on migration. Within controversies on Muslim ‘others’, ethnicity and gender frequently interweave. In discussions about the Muslim woman, a problematic dichotomy frequently emerges: namely the representation of a Muslim woman who wears the headscarf and is seen as ‘oppressed’ or ‘traditional’. This is opposed to the position of a Muslim woman who does not wear the headscarf and is simultaneously considered a ‘self-determined’ or ‘modern’ Muslim woman. Against this backdrop, this contribution adopts a critical perspective on dichotomising discourses on Muslim women’s practices in relation to wearing the headscarf. In this article the authors examine narrative interviews with four Muslim women, focusing on their subjective experiences and psychological coping strategies with dichotomous and dichotomising stereotypes. An in-depth qualitative analysis shows that these women display a need to constantly justify and negotiate their own positions in relation to wearing the headscarf, regardless of whether the interviewed women actually wear a headscarf or not. Based on this, the authors identify different psychological coping strategies and discuss them critically in a wider framework that draws attention to existing social hierarchies.

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.503

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.069
GPT teacher head0.330
Teacher spread0.261 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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