Fairness Perceptions and Experiences of Muslim University Students in Canada
Bibliographic record
Abstract
The purpose of this article is to examine the perceptions and experiences of fairness amongst Muslim post-secondary students based on our gathering of data using a web-based survey. The participants, 189 Muslim students, were reached via student organizations, national and local Muslim organizations, and Muslim student groups organized on Facebook. Following these initial contact points, snowball sampling was used to invite prospective participants to respond to the quantitative items in the survey instrument (which also included qualitative inquiries). These quantitative responses were analyzed using descriptive statistical analysis techniques. For Muslim students, their university was perceived as the most fair amongst their experience of settings, followed by Canada in general, and the country that these Muslim students culturally most identified with. The World, at large, was perceived as the most unfair setting for responding Muslims. Except for the country that Muslim students culturally identified with, all settings were perceived to be fairer for non-Muslims than for Muslims. The majority of Muslim students reported that they had encountered, observed, or experienced unfairness at least once in their university settings during the previous academic year and that they had been impacted by that experience of unfairness.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.004 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".