The impact of the COVID 19 pandemic on motivation to stay: a comparison between Chinese and non-Chinese international students in Nova Scotia, Canada
Bibliographic record
Abstract
The COVID-19 pandemic has disproportionately impacted international students due to unexpected changes in policies and regulations regarding their visa status and immigration, travel restrictions, and heightened scrutiny against foreigners. Such changes potentially disrupt and affect international students’ post-graduation migration plan: whether they decide to go back to their home country or stay in Canada and apply for permanent residency. This may particularly be the case for Chinese international students, the 2nd largest group of international students in Canada, due to the rise of anti-Asian racism and the stigmatization that the COVID-19 is a ‘Chinese virus’. Using ‘intellectual migration’ as our analytical framework, we pay particular attention to the experiences of Chinese international students in the province of Nova Scotia, an intellectual periphery in Canada. Drawing upon data from online surveys and focus groups, this study compares the experiences of Chinese and non-Chinese international students during the pandemic and whether these experiences have impacted their post-migration plans and their motivation to stay in Nova Scotia, Canada.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".