Unveiled Muslim Women and Intersectionality Within Windsor's Muslim Community
Bibliographic record
Abstract
Previous academic research suggests that dominant Islamic discourses about gender police Muslim women to dress modestly by veiling, lowering their gaze in front of men, being obedient, chaste, and sexually pure. However, Islamic discourses are not always performed and embodied in the same ways, especially in terms of veiling and dress code. In fact, it has been found that Muslim women who do not veil are often marginalized because it is assumed they are rejecting their Islamic duty to veil, especially those living in the West. This has been linked to growing rates of Islamophobia within Western countries and the ways in which minority Muslim communities have become more guarded and conservative in order to openly mark their religious affiliation (McGuinty, 2014). Based on this information, I expected to find similar experiences in my population of Muslim women in Windsor, Ontario. The following qualitative research study examines the intersectional experiences of unveiled Muslim women within the minority Muslim community in Windsor, Ontario. Through semi-structured interviews, this study collected data from 5 unveiled and identifying Muslim women participants attending University of Windsor who were between the ages of 20-30. The importance of this qualitative study lies in uncovering the real lived experiences of these women through an intersectional feminist approach which addresses gender, race, and religious identity. By using the feminist methods of excavation and inclusion, this study analyzes those experiences of unveiled Muslim women in hopes of revealing a better understanding of what normative femininity looks like within Windsor’s Muslim community, and to produce data that will encourage political and social changes that benefit these women.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.011 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".