Power, Politics, and Education: Canadian Universities and International Education in an Era of New Geopolitics
Bibliographic record
Abstract
This paper focuses on the recent political spars between Canada and Saudi Arabia as well as China and their impact on Canadian universities. It asks three questions: (1) What key issues did Canada’s political strains with Saudi Arabia and China raise for Canadian universities’ international education (IE) initiatives and what issues were absent? (2) What do these key issues suggest about Canada’s approaches to IE in an era of new geopolitics? and (3) What implications can be drawn from these cases about Canadian university-government relations in the context of new geopolitics? Given the powerful role media plays in education policy, a systematic study was conducted across three main media sources to identify 74 articles and news releases between August 2018 and November 2019. Three dominant themes are identified and analyzed, each vividly illustrating the close ties between global politics, government foreign policy and IE within Canadian Universities. On the one hand, the narratives speak to concerns about IE as a risk to national security and, on the other, as a vehicle for Canada’s economic prosperity. However, what the media has not achieved is a broader discussion on how Canada needs to revisit its IE objectives and approaches in light of broader geopolitical shifts. Using the theoretical framework of soft power, the paper speaks to the limitations and short-sightedness of Canada’s approach to IE as soft power in this era of new geopolitics and concludes with three recommendations for 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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.030 | 0.025 |
| Scholarly communication | 0.026 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".