Bibliographic record
Abstract
In recent years, Canadian colleges have seen a significant rise in international student enrolments, particularly in Ontario where the majority of international students in 2020/2021 came from India (62 percent), followed by China (6 percent) and Vietnam (4 percent). As a result, Ontario colleges are playing an increasingly important role in shaping education migration pathways, developing the future labour force, and influencing internationalisation policies and practices in Canada. However, with this growth come concerns about the social and economic challenges faced by international students and the role of colleges in addressing these issues and supporting these students. In this context, this timely study was conducted to produce both theoretical and empirical insights into the challenges faced by international students while studying in colleges and after graduation, as well as the services available to address these challenges. Data was collected through literature reviews and interviews to better understand the existing knowledge of international college students and their lived experiences. The findings of the study reveal that international students face challenges in accessing reliable information, integrating academically and socially, integrating into the labour market, and transitioning to permanent status in Canada. Unfortunately, these students receive little support from both colleges and the government. The study suggests that international students require greater support from colleges and all levels of government during and after their studies in Canada, as they seek employment and permanent Canadian residence. It is crucial to address the social and economic challenges faced by international students and to provide them with the necessary support to overcome these challenges and succeed.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.031 | 0.006 |
| Scholarly communication | 0.012 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".