Reshaping the landscape: considering COVID-19’s uncertain impacts on Canadian and U.S. international higher education
Bibliographic record
Abstract
As the world grapples with the COVID-19 pandemic, international higher education (IHE) enters a new territory and complicates models that describe a third wave of internationalization. Against this backdrop, we apply a three-layer (country, institution, individual) analysis to understand COVID-19’s impact on IHE in Canada and the United States, on particularly student mobility, and consider the future of an altered landscape. At the national level, we consider how the two countries are responding to COVID-19 regarding their policies toward international students and what long-term impact might be looming. At the institutional level, we consider the pandemic’s impact on institutions’ revenue, mission, internationalization strategies, and even survival. At the individual level, we examine how this pandemic impacts international students’ plan of study in Canada and the United States, with their concern for the expense and experience of online learning and their consideration of other alternative destination countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".