The gendered nature of Muslim and Christian stereotypes in the United States
Bibliographic record
Abstract
Despite the increasing diversity of religious affiliations in the United States, little research has explored the nature and structure of religious stereotypes of Muslims in America. The present research explores the gendered dimensions of stereotypes of both Muslims and Christians, using a multimethod approach. In Study 1, participants engaged in visual representations of intersectional and superordinate identities using Venn diagrams and slider tasks. Study 2 elicited open trait listings for religious, gender, and intersectional groups, with the most common traits reported for each group. In a conceptual replication, Study 3 asked participants to rate each group for the applicability of the most common traits identified in Study 2. Across the three studies, we found clear and consistent support for intersectionality effects. Unique stereotypic traits were identified for each intersectional group that were not present in either religious or gender superordinate identity. Stereotypes of Christians as a superordinate group contained a balanced representation of Christian men and Christian women traits. In contrast, Muslim stereotypes were strongly influenced by androcentric assumptions, with approximately 80% of the traits ascribed to Muslims overlapping with those of Muslim men. In addition, Muslim women were rated as significantly different from both Muslims and Muslim men on all trait evaluations. This was not observed with Christians, who showed little differentiation by gender. This research provides a rare systematic analysis of the gendered nature of religious stereotypes of Christians and Muslims and contributes to the developing literature on intersectionality and prototypicality.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".