Is There Adversity in Diversity? Racialized International Students' Experiences at a Southwestern Ontario University
Bibliographic record
Abstract
Given the anticipated decline in enrollments of international students (IS) for the years 2024-2026, particularly in light of the shifting policy landscape and the evolving educational climate, it becomes increasingly pertinent to revise our understanding of the experiences of racialized IS in Canada, especially within the context of postsecondary institutions in Ontario. Despite the institutional prioritization of internationalization and principles of Equity, Diversity, Inclusion, Decolonization (EDID), and anti-racism efforts, there remains a significant gap in comprehending the nuanced realities faced by these students—realities that could be further impacted by changing demographics and policies. Therefore, this study aims to critically explore the implications of these transformations on the lived experiences of 8 racialized international students while contributing valuable insights to the discourse surrounding inclusion and exclusion within the academic environment. Even though 77% of Canadian universities have confirmed that EDID policies are integrated seamlessly within strategic plans and documents (Charbonneau, 2019), the extent to which these policies foster the inclusion of racialized IS is a topic that warrants more attention, especially in the current climate of shifting Canadian IS immigration policies towards anti-international sentiment. This case study, conducted at a public university in southwestern Ontario, provides a comprehensive approach to understanding the experiences of racialized IS within the framework of EDID and institutional anti-racism policies. The study collected data by examining institutional documents and conducting semi-structured interviews with senior administrative leaders from the EDID and international offices. Additionally, it included semi-structured interviews with a diverse sample of 8 racialized IS from the global south and east. These interviews provided insight into their experiences during the university's efforts to promote equity, diversity, and inclusion. Existing gaps can be ascribed to the underpinning of the current model of EDID infrastructure, which is predicated on a Euro-Western perspective understanding of diversity. The current model can do better to address global markers of discrimination that are perhaps unbeknownst to governing senior institutional leaders, thus identifying critical contradictions and inherent complexities within the EDID and anti-racism infrastructure. The findings of this research illuminate the need for a transnational and intersectional approach to diversity and inclusion as one way of achieving equity. The key findings from experiences of racialized IS highlight that EDID and anti-racism policies are more about rhetoric and performance rather than actively promoting meaningful inclusion for diverse students with varying intersectional identities who have unique challenges. As a result, these superficial EDID and anti-racism efforts unintentionally uphold existing inequalities and power imbalances. This includes domestic students being hesitant to interact with IS, ostracized, and the ineffectiveness of EDID and anti-racism training. The absence of coordinated efforts between the international and EDID offices that are siloed exacerbates racialized IS exclusionary experience further, ultimately undermining the effectiveness of the institution’s EDID efforts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.049 | 0.020 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 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".