Learning to Teach While Muslim: Examining Muslim Teacher Candidates’ Experiences in Canadian Teacher Education
Bibliographic record
Abstract
This critical qualitative study examined the experiences of a small group of Muslim teacher candidates (TCs) and the various challenges they encountered while enrolled in teacher education programs in Ontario. Semi-structured interviews and focus groups were conducted with Muslim TCs at three Ontario universities, and interviews were also conducted with faculty and staff nominated as supportive by Muslim TC participants. Findings are situated within a conceptual framework bringing together Islamophobia, Orientalism, anti-Brown racism, and tenets of critical race theory (CRT). The first findings chapter examined microaggressions experienced by Muslim TCs in their teacher education, including those perpetrated by peers, professors, associate teachers, K-12 students, and program administrative staff. The following three themes are explored: (1) participants’ experiences of implicit Islamophobia as largely microaggressive and occurring overwhelmingly on practicum (as opposed to teacher education coursework); (2) faced stereotype-based expectations, including assumptions about their unfamiliarity and illegitimacy within the teaching field, and ostracizing Muslim TCs; and (3) assumptions of homogeneity, including assumptions that Muslim TCs are homophobic or transphobic, or necessarily devout; or assumptions that Muslim women are oppressed and silenced. The second findings chapter identified five overarching themes that reflect macro or structural barriers to Muslim TCs: (1) religious practices and a lack of accommodations; (2) Eurocentric curriculum as a structural barrier; (3) a lack of consideration for TC demographics in practicum placement; and (4) professionalism discourse. A third findings chapter examined various strategies employed by Muslim TCs in response to the challenges they face in their courses and on practicum. These are: (1) creating a counterspace through the support and solidarity offered by formal identity-congruent groups or clubs; (2) relying on informal support networks by connecting with other racialized TCs; (3) developing a ‘thicker skin’; (4) being assertive in countering Islamophobic stereotypes or other forms of racism; and (5) finding solace in prayer and spirituality. Lastly, a discussion chapter engages with various concepts employed in this study (i.e., Islamophobia, Orientalism, anti-Brown racism, and various tents of CRT), and examines how these concepts contribute to addressing the research questions. The dissertation concludes with a set of recommendations for policy and practice.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.021 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 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".