Experiences of Black International Graduate Students: Encounters of Racial Disparities amidst EDI Rhetoric at a Canadian University
Bibliographic record
Abstract
International students are integral to Canadian higher education institutions and Canada’s economic gain, contributing $37.3 billion to the economy in 2022, up from $21.6 billion in 2018 (Statistics Canada, 2024). They also add to the cultural enrichment and diversity of institutions and are a major source of bridging the gap in Canada’s labour shortage. This research explored the experiences of Black graduate international students. Grounded in Vygotsky’s sociocultural theory, the researchers centred the voices and perceptions of 12 Black international graduate (BIG) students as they shared their university experiences in a foreign land, rife with unfamiliarity/anomalies. Data were gathered using focus groups and semi-structured interviews to address the question, “What are the experiences of BIG students in the contexts of race, equity, and student support?” Participants candidly shared their university experiences. Interpretive phenomenological analysis (IPA) was used to understand how their lived experiences influenced/impacted their transition and academic milieu. The findings highlighted the emotional effects on participants as they navigate the nuances of international education and suggest the need for increased dynamic student support. Recommendations were made that would contribute to knowledge sharing and empowering universities, particularly student services units, to better understand and better respond to the needs of BIG 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 | 0.007 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.054 | 0.024 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.004 | 0.009 |
| 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".