The Muslimah Project: A Collaborative Inquiry into Discrimination and Muslim Women’s Mental Health in a Canadian Context
Bibliographic record
Abstract
Prior research in Europe and North America demonstrates that religious discrimination against Muslim people, commonly known as Islamophobia, results in many negative mental health impacts, including depression, anxiety, isolation, and feelings of exclusion (Awan & Zempi, 2015). In Canada, Muslim women face a unique form of discrimination based on their religious, racial, and gender identities (Helly, 2012; Zine, 2008). Grounded in feminist intersectional theory and practice (Hill Collins & Bilge, 2016), the present manuscript emerges from a community-based project centered on Muslim women's experiences of discrimination and resulting adverse mental health impacts. Through a series of five focus groups (N = 55), the research team engaged with Muslim women from diverse backgrounds in order to gain a more complete understanding of mental health inequities in Canada. Thematic analyses of focus group data revealed that Muslim women participants regularly experience Islamophobic discrimination and face multiple barriers when attempting to access culturally relevant and responsive supports. Results illuminate the potential of reciprocal, community-based research to investigate and respond to mental health disparities experienced by Muslim women in Canada.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.039 | 0.009 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| 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".