Transculturality, Anti‐Asian Racism and Student Mobility: A Case Study of Chinese International Student Experiences during the COVID‐19 Pandemic<sup>1</sup>
Bibliographic record
Abstract
The breakout and spread of the SARS‐COV‐2/COVID‐19 virus in early 2020 caused a drastic increase in blatant racism and microaggressions against Asians/people of Asian descent. The rise of anti‐Asian racism can be viewed as a repeat of a century‐long narrative of Yellow Peril against Asian newcomers in western societies. While some reports/studies explained the phenomenon with the hegemony of race relations, it is imperative to examine the experiences of anti‐Asian racism in the context of the fast‐changing geopolitical economy and transcultural relations. Using the conceptual frameworks of Intellectual Migration and transculturalism, this study examines how the rise of anti‐Asian racism during the COVID‐19 pandemic affected Chinese international students in Nova Scotia and if their experience of racialization was critical enough to change their post‐graduation plan of staying in Canada or not. In order to provide an overview of international student experiences in Nova Scotia and to assess the significance of ethnicity and racism as factors in student mobility, we analyzed the data from two research projects. First, survey and individual interview data from the IM (Intellectual Migration) Halifax project provided detailed insights on Chinese international students' study and living experiences during the pandemic and their post‐graduation plans. Second, survey and focus group data from the NSIS (Nova Scotia International Students) project allowed a comparison of pandemic experiences between Chinese and other international students in the province of Nova Scotia. This case study aimed to examine the experiences of racialization among Chinese international students in Nova Scotia and assess the extent to which their post‐graduation mobility is shaped by racialization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".