International Students in Canadian Higher Education: Ethical Challenges in Employment and Financial Stability in the Greater Toronto Area
Bibliographic record
Abstract
This study investigates challenges international students face in the Greater Toronto Area (GTA), Canada, focusing on employment, financial stability, and academic performance. It tests five hypotheses: whether higher education levels enhance employment status, if job search difficulty increases over time, whether employment affects academic performance, if employment income covers living expenses, and whether students plan to change jobs post-graduation. A mixed-methods survey in January 2025 targeted 124 international students across diploma, bachelor’s, master’s, and PhD programs in the GTA, with questions on employment status, job search experiences, financial status, academic impacts, and career aspirations. Data were analyzed using Python with descriptive statistics and chi-square tests. No significant link was found between education level and employment (p=0.570), with 62.9% employed. Most (71.8%) reported greater job search difficulty over time due to labor market competition. Employment did not significantly impact academics (p=0.258), with 38.5% unaffected. Financially, 46.2% could not cover expenses (p=0.134). Post-graduation, 74.4% planned to seek better jobs (p=0.196). These findings highlight ethical concerns about equitable access to employment and financial support, urging institutions to address systemic barriers to fair treatment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.019 | 0.007 |
| Scholarly communication | 0.006 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".