Impact of Islamophobia on post-secondary Muslim students attending Ontario universities
Bibliographic record
Abstract
This study investigated the experiences of Muslim students attending Canadian institutions of higher education in the context of increasing Islamophobia. Qualitative semi-structured interviewing was used to explore the impact of anti-Muslim sentiment on the academic experiences of Muslim students and patterns of identity construction subsequent to recent national and international terrorism-labeled events such as the 2015 Paris bombings and 2015 San Bernardino mass shooting. Analysis of interview data yielded two major themes: (a) the formation of a strong religious identity in response to experiences of Islamophobia and (b) a distinction between general Islamophobia and gendered Islamophobia. The findings suggest that Muslim students in the current post 9/11 era are becoming increasingly devout, have a strong attachment to their religious identity, and are at the forefront of advocating for Muslims through education, activism, civic participation, and interfaith dialogue.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".