The Relative Performance of International Students and Their Academic Program Choices
Bibliographic record
Abstract
Canada is increasingly looking to international students as a source of postsecondary tuition revenues and new immigrants. In Chapter 1, we examine the relative course grades of international undergraduate students in an Ontario university with a large and growing foreign student presence. We identify grade gaps across fields of study, which appear to primarily reflect admission errors from less predictive secondary school grades. While the gaps appear related to English-language proficiency, they are larger among graduates of Canadian secondary schools and in upper- than in first-year courses. Our estimates also suggest that relative foreign student quality has improved over time, despite increasing foreign enrolment. \n \nThe academic programs that students choose to pursue have strong implications for their career prospects. In Chapter 2, I shed light on students’ academic program choices by examining how co-ethnic peers influence their decisions to change programs during the course of their undergraduate studies. Examining data from a publicly-funded Ontario university with an ethnically diverse student population, I find that students are highly ethnically concentrated within academic programs at the time of their initial enrolment. Moreover, nearly one-quarter of all students change programs at least once during their studies and these program changes further increase the ethnic concentration of students within academic programs. Assuming a model in which students prioritize their grades over co-ethnic peers, the presence of more co-ethnic peers is found to significantly increase the \nprobability of a program change. This suggests that the ethnic concentration of students across programs at the university, which appears to increase over time, may be academically and socially efficient. International students are considered to be the best source of immigrants. \n \nIn chapter 3, we compare the labour market performance of former international students (FISs) through the first decade of the 2000s to their Canadian-born-and-educated (CBE) and foreign-born-and-educated (FBE) counterparts. We find FISs outperform FBE immigrants by a substantial margin, but underperform CBE graduates from similar postsecondary programs. We also find evidence of a deterioration in FIS outcomes relative to both comparison groups. We argue that this deterioration is most consistent with a quality tradeoff as the supply of international students has not kept pace with the growth in demand.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".