Hijab and enclothed cognition: The effect of hijab on interpersonal attitudes in a homogenous Muslim-majority context
Bibliographic record
Abstract
Stereotyping and discrimination against hijab-wearing women have been studied extensively in many Western countries, which are home to Muslim diasporas. However, there is a paucity of research on Muslim-majority countries. The purpose of this study is to address this gap and explore interpersonal attitudes toward both hijab-wearing and non-hijab-wearing women, in Pakistan, a Muslim Majority country. In this paper, we used the presence or absence of hijab as the independent variable, and measured competence and warmth using items from the Stereotype Content Model (SCM), as well as social and task attraction using items from the Interpersonal Attraction Scale (IAS) as dependent variables. Study 1 included 352 undergraduate students, while Study 2 involved 151 human resource professionals. The findings from both studies were consistent in suggesting that participants had a higher attribution of competence, warmth, and social and task attraction toward the hijab-wearing women compared to the non-hijab-wearing women. Conversely, participants in the non-hijab condition attributed lower levels of warmth, competence, and social and task attraction. We interpret these findings such that in a homogeneous society, individuals who strongly identify with and internalize Muslim culture, and exhibit a preference for their own cultural and religious values (cultural endogamy), attribute higher levels of competence, warmth, social attraction, and task attraction to the protagonist who wears hijab. This research has implications for employment opportunities and attitudes toward women in the workplace in Muslim-majority countries, both for hijabis (women who wear a headscarf) and non-hijabis (women who do not wear hijab).
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".