Problematizing Access to Higher Education for Refugee and Globally Displaced Students: What’s the Problem Represented to Be in Canadian University Responses to Syrian, Afghan and Ukrainian Crises?
Bibliographic record
Abstract
The UNHCR’s 15by30 campaign to increase refugee student enrolment in higher education to 15% by 2030 is a lofty goal. Canadian higher education institutions have a role to play in contributing to this policy goal, along with advocacy efforts from refugee student groups, community-based organizations, government, and international organizations. The aim of this study is to look critically at how the issue of access to higher education for refugee and globally displaced people is represented through Ontario’s universities’ responses to federal government initiatives to crises in Syria, Afghanistan and Ukraine. In this study, we use Bacchi’s (2009) “What’s the problem represented to be?” approach to policy analysis and, drawing on Dillabough’s (2022) critique of modernity in higher education, we argue that university responses related to refugee and globally displaced student access to higher education offer the possibility to reflect on the paradoxical tensions of the problem space in Canadian higher education. In our findings, we discuss how the problem of refugee and displacement crisis was represented differently in response to differences in geopolitical conditions and government policies, as we demonstrate how representations of material problems and categories of “citizenship” and “geographical location” in the universities’ responses contributed to creating boundaries of inclusion and exclusion for access. Finally, we show how the creation of educational programs for “globally displaced people” during the period related to the Ukrainian crisis perpetuates the logic of colonialism in the universities’ responses.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.044 | 0.037 |
| Scholarly communication | 0.022 | 0.008 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".