Structural Factors Associated with Higher Education Access for First-Generation Refugees in Canada: An Agenda for Research
Bibliographic record
Abstract
Refugees are the least educated migrants upon arrival to Canada. Yet, they invest in Canadian higher education at lower rates than other newcomers. Why might this be? This paper enters this emergent conversation through a review of the Canadian-based empirical literature on the structural factors associated with refugees’ tertiary education access. Research indicates that as part of the low-income population, refugees are likely to misperceive the cost and benefits of higher education and be deterred by high tuition costs. Academic preparedness and tracking in high schools also pose additional constraints. The gap in the literature exposes a need for inquiry into the ways in which pre-arrival experiences influence refugees’ participation in Canada’s post-secondary institutions. The paper concludes by underscoring the need for qualitative research that discerns the lived experiences of refugees outside of the aggregate immigrant grouping typical in education research.
Stored with the screening record, where it is evidence for the labels above.
How this classification was reachedexpand
The three-model screen
all 5,600 screened works →All three models called this out of scope.
Literature review of structural barriers to higher education access for refugees in Canada, closing with a research agenda; the substantive object is refugee educational access rather than research practice, although its gap analysis and critique of aggregate immigrant categories edge toward the boundary.
This review concerns refugees' access to higher education, not how research is conducted or evaluated.
Agenda paper on refugees’ access to tertiary education; student access, not the research workforce or research practice.
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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".