Should I Stay or Should I Go? International Students’ Decision-Making About Staying in Canada
Bibliographic record
Abstract
Recent decades have seen an increase in the popularity of international education. Approximately 500,000 international students were in Canada in 2018 and this number is projected to grow. While we know that many international students decide to stay in Canada, we do not fully understand the decision-making process employed by international students regarding staying in Canada or going back home after completing their education. The purpose of this study was to explore how international students make decisions about their post-graduation destination and what factors they see as pivotal in shaping their decision-making process. We utilized a symbolic interactionist approach to analyze qualitative semi-structured interviews with 60 international students enrolled in post-secondary programs in Canada. Our findings suggest that the meaning students attach to staying in Canada varies from obtaining permanent residency to working for a few months upon graduation. We also demonstrate that for most students, the decision to stay in Canada is formed gradually and is shaped by familial obligations, cultural climate they experience in Canada, employment opportunities available to them upon graduation, and the possibility of obtaining permanent residency.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.012 |
| Scholarly communication | 0.008 | 0.001 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".