Mirrors and Reflections: Perceptions of Muslim Immigrant Women in Toronto
Bibliographic record
Abstract
This study addresses the perceptions that Muslim immigrant women in Canada have about themselves, as self-perception, while also speaking to how they perceive others’ views that affect their lives. Drawing on the experiences and perceptions of self-identified Muslim immigrant women in Toronto, who are English language learners, this study aims to understand Muslim immigrant women’s self-esteem. The study seeks to understand how they perceive themselves, how they simultaneously perceive how others see them in Canada, and the impact of such a double reflexive process. The study examines the cultural and religious impacts on immigrant Muslim women that lead them to deal with a certain “double consciousness” (Du Bois, 1968) to fit into a dominant Canadian culture shaped by colonialism, patriarchy and anti-Muslim racism. Specifically, the study investigates the challenges and needs encountered by Muslim immigrant women in Canada due to existing Orientalist stereotypes and biases about the Muslim community, and how they navigate these challenges. This study adopts a mixed methodology grounded in autoethnography, interviews and a focus group, reflexivity and poetry expression, and creative imagination and visualization. While the study is based on a limited sample, results revealed that Muslim immigrant women’s response to the racist and patriarchal gaze does not diminish their self-esteem; instead, the key finding is that their sense of themselves as agentic, confident women is strong, and this self-perception is not lowered by the perceptions of others.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".