Ambivalent Mobilities? Social Resilience and the Experiences of International Students in Canada
Bibliographic record
Abstract
Canada has turned to recruiting growing numbers of international students as part of its broader neoliberal “managed migration” strategy designed to realize economic goals. Canadian universities have come to the fore as key players in the active recruitment of students from abroad. In this paper we draw on the analytic concept of “social resilience” to examine the role of universities in developing structures and mechanisms that promote and/or hinder international student resilience. We highlight the contradictory role that universities play vis-à-vis international students. On the one hand, international student fees are increasingly important to universities to address budget shortfalls engendered by neoliberal policy prescriptions. Consequently, universities are active recruiters of international students in a global marketplace. But on the other hand, international students have complex needs as newcomers to Canada that are not addressed by existing settlement services; universities are thus increasingly pressed to fill in and provide them with various supports. Our qualitative case study is framed by this contradiction and concentrates on two Ontario universities located in the same city, Ottawa, to develop a case-study analysis of student experiences. This paper offers a counter narrative to familiar neoliberal policy rationales that discursively construct international students as human capital to be harnessed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.065 | 0.035 |
| Scholarly communication | 0.014 | 0.003 |
| Open science | 0.003 | 0.014 |
| Research integrity | 0.003 | 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".