Magnets, gatekeepers, surveillants, and refiners: The emergence of higher education institutions as migration governance actors in Australia, Canada, and Germany, 1990 to 2019
Bibliographic record
Abstract
• The globally circulating policy discourse ‘ international students are ideal immigrants’ merges three policy arenas: economic immigration, higher education, and international student mobility. This is part of a relatively new edugration (education + [im]migration) policy context in which international students transition to permanent residency status after graduation through targeted immigration pathways. • Edugration has been translated into policy, to varying degrees, across much of the Global North in the 21st century, including Australia, Canada, and Germany. • In this edugration policy context, we argue that higher education institutions now play migration governance roles as magnets, gatekeepers, surveillants, and refiners of future economic immigrants to different degrees in a variety of countries. • These roles are justified by federal governments through a variety of discursive legitimization strategies. However, limited attention has been paid to the imposition of governments’ immigration-focused rationales. • Through a critical discourse analysis of federal government policy documents, we show how the four migration governance roles identified here emerged at different times, yet in relatively similar ways, across Australia, Canada, and Germany. The globally circulating policy discourse ‘ international students are ideal immigrants ’ merges three policy arenas: economic immigration, higher education, and international student mobility. This is part of a relatively new edugration (education + immigration) policy context in which international students transition to permanent residency status after graduation through targeted immigration pathways. We utilize critical discourse analysis to systematically examine select federal policy documents during critical time periods in edugration policy formation across three comparator countries (Australia, Canada, and Germany) from 1990 to 2019. In examining the ripple effect of international student mobility as it intersects with immigration policy, we show the impact of this policy discourse on higher education institutions: in edugration contexts, higher education institutions play new migration governance roles as magnets, gatekeepers, surveillants, and refiners of future economic immigrants. This raises broader questions regarding the long-term implications of international student mobility for university-government relations and the societal roles of higher education institutions more generally.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".