International Student Mobility, Government Policies, and Neoliberal Globalization
Bibliographic record
Abstract
Given the increasing magnitude of international student flows from “developing countries” to the “developed” or major member countries of the Organization for Economic Co-operation and Development (OECD), this chapter explores Chinese graduate student flows to Canada. Chinese graduate student perspectives are also drawn upon to study the phenomenon of Chinese student migrations to Canada in pursuit of higher education. Given the focus on exploration, meanings and understandings, an interpretivist approach and qualitative case study strategy were utilized to examine government policies and positions that stimulate international student mobility (ISM) from China to Canada and to understand the experiences of Chinese graduate students who study at the University of Alberta. Unlike the ISM mainly sponsored by the Chinese government before, contemporary outbound student mobility is impacted by neoliberalism and a freer mobility shapes Chinese students' pursuit of overseas studies. Chinese traditional culture and values also influence Chinese student mobility across borders for pursuing higher education. In the meantime, patriotism makes many Chinese students concern about serving their home country. The chapter also presents reflections on government policies pertaining to ISM and highlights the emergent themes from the data obtained from the qualitative case study of Chinese graduate student flows to 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".