Factors That Influence Canadian Students' Choice of Higher Education Institutions in the United States
Bibliographic record
Abstract
This study seeks to explain the factors influencing Canadian students’ motivation for studying in the United States. The United States has continuously been the leader in international students, but is now facing increasing competition from other nations around the world. As one of the top senders of international students to the United States, Canada is of special interest to institutions of higher education in the United States due to the close social, economic, and political ties. International student mobility is influenced by push-pull factors that influence a student’s decision to study abroad, and ultimately pull factors from host nations that student’s find favorable. To understand Canadian students’ motivation for studying in the United States, a study was conducted with a sample of 411 Canadian students at a small private college in Buffalo, New York. The study found statistically significant differences in the the importance placed on reasons for not remaining in Canada for higher education, the factors that influence the selection of the United States as a study destination, and student preferences for institutions of higher education in the United States. The study concluded that there are separate push-pull factors influencing Canadian higher education students on the Canada-U.S. border, and that Canadian students are their own distinct group that are neither truly like international nor domestic students.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".