Bibliographic record
Abstract
Canadian universities’ sharpened focus on international students starting in the early 2000s coincided with the growing interest by students from China to study abroad. Various actors, including states, have shaped and benefited from this increase in student migration. I examine how student migrants deal with the feeling rules transmitted to them, as an under-explored site where the migration experience is shaped and justified. In light of the work of Sara Ahmed and Arlie Russell Hochschild, I explore how students feel and are asked to feel about their studies abroad, and how emotions work in framing and maintaining the migration narrative. Through Ahmed’s concept of skin of the collective, I argue that Chinese student migrants are affected by and contribute to an affective atmosphere regarding their years of study in Canada as specific feeling rules help them make sense of similar experiences of confusion, frustration, self-reliance, and responsibility. Based on interviews with students and university staffers, I discuss the links between this type of migration, the actors involved, and the emotional landscapes students navigate in order to highlight how they interpret their own experiences and how these interpretations contribute to maintaining a general narrative about being Chinese international students in Canada.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.024 | 0.037 |
| Scholarly communication | 0.017 | 0.008 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".