Racialized International Students and Their Experiences in a Canadian University During COVID-19 Pandemic
Bibliographic record
Abstract
Internationalization is part of Canadian universities' strategic priorities.Recruitment of international students is key to universities' operational budget to compensate for budget cuts caused by a neoliberal emphasis on transparency, efficiency, productivity, and cuts in public spending.The latter measures have resulted in an increased enrolment of international students, primarily from India and China (Crossman et al., 2023), as well as the rise of international student tuition fees at Canadian universities.This research centers on racialized students' experiences during the COVID-19 pandemic.It focuses on their voices and concerns with racism and microaggression as well as other challenges they have experienced during these critical times.Drawing on a case study methodology (Yin, 2014) and using semi-structured interviews, we interviewed six racialized students at one university in Ontario, Canada.The findings show that measures taken by provincial and federal Canadian governments lead to remote learning, isolation and, surprisingly, mitigated experiences of overt racism on university campuses.However, participants expressed instances of microaggression through social media that targeted mainly racialized students from China.They also highlighted the financial difficulties they encountered and the lack of institutional support that caused them emotional and mental stress which consequently impacted their academic progress.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.085 | 0.017 |
| Scholarly communication | 0.018 | 0.004 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.005 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".