The gendered nature of Muslim and Christian stereotypes in the United States
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it