An Investigation of the Potential Microaggression of International Student's Experiences on a Canadian University Campus
Bibliographic record
Abstract
With an increasing number of international students coming to Canada, the retention of these students has become a significant topic of concern. The persistence and success of international students are largely influenced by their sense of belonging, which is associated with multiple factors, including discrimination and microaggression. This study was designed to explore international students’ experiences with microaggressions and to share student-led recommendations to assist faculty and the university at large. The study applied a qualitative research method that interviewed 14 international students. Four key themes emerged from the findings: microaggression experiences, the influence of microaggression on the sense of belonging, coping with microaggression, and suggestions to minimize microaggression. These findings from the study highlight the intersectionality of microaggression experienced by international students at university campuses. By implementing the recommendations provided by the study, including addressing cultural bias, prioritizing students' well-being over profit, revising and reviewing policies, including diversity in staff and faculty, and implementing comprehensive equity, diversity, and inclusion (EDI), promote open communications, enforce accountability and consequences universities can work towards creating a more inclusive, supportive, and equitable environment for all students, staff, and faculty members.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.032 | 0.010 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 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".