The potential of Canada's international student strategy: Evidence from the "MIT of the north"
Bibliographic record
Abstract
A key objective of Canada's International Education Strategy (2014) isto leverage Canada's postsecondary institutions to attract and retain the world's "best and brightest" students to raise the average skill of the Canadian population and boost economic growth. However, evidence suggests that Canada's former international students experience significant labour market integration challenges, and the Strategy overlooks these challenges. The earnings disparities of former international students, and Canadian immigrants more generally, are usually interpreted as evidence of skill underutilization owing to employer discrimination against racial and ethnic minorities. Hard evidence of skill underutilization is, however, scant due to a dearth of data providing direct measures of workers' skills. Our study brings new evidence to bear on the skill underutilization hypothesis by examining a unique linkage of student records from the University of Waterloo, including students' grades, with immigration data from Immigration, Refugees, and Citizenship Canada and T1 income tax returns from the Canada Revenue Agency. UWaterloo is best known for its academic programs in computer science, mathematics, and engineering, which has earned it the moniker the "MIT of the North." Evidence that UWaterloo's international student graduates struggle in Canadian labour markets relative to their Canadian-born counterparts graduating from the same academic programs with similar academic standing provides a direct test of the skill underutilization hypothesis. The evidence also offers critical lessons on whether policy efforts to realize the full economic potential of international students are best directed at augmenting employer hiring behaviour through DEI initiatives, for example, or at improving the attraction and selection of international talent and promoting skill formation, including language training.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".