The impact of COVID‐19 on international student enrolments in North America: Comparing Canada and the United States
Bibliographic record
Abstract
Abstract Both Canada and the United States enrol a significant number of international students. However, in March 2020, both countries closed their borders and increased restrictions to international travel due to COVID‐19, which had a direct impact on international students' ability to travel between their home countries and study destinations. This article examines the impact of COVID‐19 on international student enrolments by asking two related questions: first, how did government policy address international students' difficult reality in the wake of COVID‐19? And, did international student enrolments change as a result? With regard to policy, we find a stark divergence: Canada's federal policies quickly adapted to support international students and ensure they remained eligible for post‐graduate work permits, preserving the appeal of Canada as a study destination. Meanwhile, in the US, federal policies for student visas required international students to maintain physical presence, reflecting a more hostile stance towards immigration, characteristic of the Trump administration. Despite these differences, with regard to enrolments, we find largely similar patterns, with COVID resulting in only a small decline in international student enrolments nationwide. A more worrying trend for both countries is that selective institutions seem to have been less impacted than access‐oriented institutions.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| 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".