Towards a More Just Canadian Education-migration System: International Student Mobility in Crisis
Bibliographic record
Abstract
Education-migration, or the multi-step recruitment and retention of international students as immigrants, is an increasingly important component of both higher education and so-called highly-skilled migration. This is particularly true in Canada, a country portrayed as a model for highly-skilled migration and supportive of international student mobility. However, education-migration remains under-analyzed from a social justice perspective. Using a mobility justice framework, this paper considers COVID-19’s impact on Canada’s education-migration system at four scales: individuals, education institutions, state immigration regimes, and planetary geoecologies. It identifies ethical tensions inherent to Canada’s education-migration from a systems-level and suggests that a multi-scalar approach to social justice can both usefully complexify discussions and introduce unsettling paradoxes. It also stresses that the COVID-19 pandemic offers an opportunity to reimagine rather than return.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.040 | 0.022 |
| Scholarly communication | 0.015 | 0.005 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 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".