Anti-Asian Racism on Campus and Chinese International Students’ Resistance in Canada: Social Media Discourse, Advocacy, and Coping Strategies
Bibliographic record
Abstract
The COVID-19 pandemic has intensified anti-Asian racism (AAR) in Canada, leading many Chinese Canadians (CC) to engage in resistance efforts (e.g., Balintec, 2022; Dao, 2021). However, Chinese international students (CIS) are often depicted as passive recipients of racism who prefer non-confrontational coping strategies (e.g., Ji & Chen, 2022). This thesis aims to challenge the dominant narrative by positioning CIS as agents of change who actively shape their campus experience. Using a case study of CIS-led and engaged in community organizing to resist a racist incident on a university campus, this study reports on conventional content analysis (CCA) of social media comments in both Chinese- and English-speaking platforms, and a 90-minute focused group discussion was held with seven CIS student activists to explore the social media discourse surrounding student advocacy, their experiences during resistance, and the impacts of social media comments on them. In addition, as a member of the group, journal entries I recorded during the period of resistance (i.e., February 1st to 14th, 2022) were included to triangulate the analysis.The findings reveal multiple narratives across the two platforms, including initial shock, surprise, anger, or hurt, and generalizations of negative impressions onto specific populations such as Canadians, CIS/CC, or all "foreigners." The online commenters divided into two sides: (1) supporting the students, believing that this incident reflects systemic problems such as racism and White supremacy, and educating others about the seriousness of this issue; and (2) blaming the students, minimizing their experience, framing them as ungrateful, or even using Chinese traditional values to discourage their resistance. An important discovery of the Chinese-speaking social media discourse is that some people in China also hold discriminatory attitudes towards CIS and other Asian populations. Through the focus group with the student activists, this thesis found that social media comments made both within Canada and China negatively impacted the CIS activists in this study, especially the disapproval and discrimination on Chinese-speaking platforms, leading them to feel "委屈 (Wei Qu)" (i.e., feeling wronged, aggrieved). Moreover, the student activists expressed a strong need for online anonymity. Despite the challenges, they continued engaging in community organizing efforts. They developed a critical consciousness around the incident thanks to their academic background and the initial reporting email setting the stage. They utilized predominantly informal support while such critical consciousness also supported them to feel like they were doing the right thing. In conclusion, this thesis contributes to a better understanding of the experiences and perspectives of CIS engaging in a resistance effort against anti-Asian racism (AAR) in Canada. It also provides policy and practice suggestions to address and support international students' activism in a Canadian setting.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.030 | 0.012 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".