‘After all, I have to show that I’m not different’: Muslim women’s psychological coping strategies with dichotomous and dichotomising stereotypes
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".