Settler nation-building through immigration as a rationale for higher education: a critical discourse analysis
Bibliographic record
Abstract
As education-migration (edugration) blurs the line between international student and immigrant recruitment in some jurisdictions, higher education admission is becoming linked to settler nation-building projects. Using critical discourse analysis, this article examines the Canadian higher education sector’s response to COVID-19 through pre-budget submissions to the House of Commons of Canada Standing Committee on Finance for the 2021 federal budget. Findings demonstrate how institutions instrumentalized international students to position themselves as valuable actors in Canada’s immigration regime and justify their requests for public financial support. In this way, nation-building through immigration – both globally, as an imperial power, and domestically, as a colonial power – is now a new societal role of higher education which is becoming hegemonic within institutions. This is significant because, as higher education’s purposes align with those of economic immigration, the sector not only fails to interrupt, but itself reproduces, systemic patterns of border imperialism and settler-colonialism. The article urges higher education institutions to (1) more deeply consider how a reliance on international student enrolment is impacting its societal roles, while also (2) avoid exceptionalizing the present by recognizing that higher education has long functioned in the service of the state as a colonial and imperialist power.
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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 | 0.015 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.037 | 0.067 |
| Scholarly communication | 0.018 | 0.007 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.004 | 0.006 |
| 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".