Searching for Equity: A Holistic Analysis of East Asian International Student Experiences and Policy Implications – A Case Study of a Canadian University Engineering Program –
Bibliographic record
Abstract
This study investigates experiences and perceptions of first year East Asian international students, faculty and staff members in the engineering program in a Canadian university and further identifies and discusses policy gaps affecting first year East Asian international students. This study used a case study approach with a document analysis and fourteen semi-structured qualitative interviews. The document analysis included university and departmental wide documents, such as University of Toronto Internationalization Strategy Document and Academic Planning as well as publicly available policy documents that were identified as gaps by the interview participants, such as Petitions and Mental Health support. The interview data were collected during the COVID-19 pandemic. All three interview groups, East Asian international students in their 1st year, faculty members and student services staff members addressed the need for more mental health support for the East Asian international students, and the faculty and staff group identified lack of culturally responsive interpretation of educational policy major gaps affecting the East Asian international students. This research used a sensemaking framework to analyze the data. In addition, the “Model Minority Myth” was used to interpret the East Asian international student experiences. In conclusion, I characterized the issues of East Asian international students as a “wicked problem” (Rittel & Weber, 1973), as the policy barriers and challenges they face are multifaceted and full of complexities. This study is significant because of the increasing number of East Asian international students in the Canadian higher education landscape. The voices of East Asian international students in scholarship have been limited and lacking, especially related to the policies affecting their experiences. University institutions and policy makers including policy implementers require a more culturally sensitive and nuanced approach in order to address the unique needs of East Asian international students and to ensure their academic and social successes in their institutions.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.039 | 0.016 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".