IMAGINE: Canada as a leader in international education. How can Canada benefit from the Australian experience?
Bibliographic record
Abstract
Hosting international students has long been admired as one of the hallmarks of internationalization. The two major formative strands of internationalization in Canadian universities are development cooperation and international students. With reduced public funding for higher education, institutions are aggressively recruiting international students to generate additional revenue. Canada is equally interested in offering incentives for international students to stay in the country as immigrants after completing their studies. In its 2011 budget, the Canadian federal government earmarked funding for an international education strategy and, in 2010, funded Edu-Canada—the marketing unit within the Department of Education and Foreign Affairs (DFAIT)—to develop an official Canadian brand to boost educational marketing, IMAGINE: Education in/au Canada. This model emulates the Australian one, which rapidly capitalized on the recruitment of international students and became an international success story. Given current Canadian higher education policy trends, this paper will address the cautionary lessons that can be drawn from the Australian case.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.044 | 0.009 |
| Scholarly communication | 0.015 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".