Challenges and Opportunities in the International Higher Education “Post-Pandemic” Landscape
Bibliographic record
Abstract
International education is a vast field of scholarship and practice. Internationalization of higher education (IHE) has been contested, debated, deconstructed, and reconstructed. While some have discussed the end of internationalization (Brandenburg & De Wit, 2011), others have discussed reimagining or rebuilding this field of practice (Stein, 2021). Since the post-World War II era, the international education sector has faced many challenges including the Cold War, 9/11 and its responses, the election of Donald Trump, and Brexit, but perhaps nothing compares to COVID-19. The pandemic has severely impacted the core of the internationalization of higher education – human mobility. CISN reached out to three IHE scholars and leading practitioners in the USA and Canada to learn about their visions for the future of IHE in the “post-pandemic” landscape. We encourage readers to send us their comments about their own responses to the following questions and their thoughts on the responses from Dr. Sonja Knutson, Dr. Harvey Charles, and Dr. Adel El Zaïm outlined here.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".