Impacts of International Education Shifts through Transnation Stories of Three Vietnamese Doctoral Students
Bibliographic record
Abstract
Abstract In response to COVID-19 global outbreaks, Canada, and Australia, two favored destinations by international students, as the contexts of this essay, have enacted different international education policies, which will be investigated through the narratives. The authors discuss transnationality and mobility as key terms in the internationalization of higher education (HE) studies through their experiences as three Vietnamese doctoral students in Canada and Australia. Transnationality is attended through a narrative of a Vietnamese returnee struggling with bringing unfamiliar knowledge of gender and sex education from the West into a Vietnamese HE context. Mobility is unpacked through stories of a Vietnamese doctoral student in Canada stuck in Vietnam due to the COVID-19 despite inviting policies from the Canadian government to international students. This experience is connected to another Vietnamese student’s experience in Australia about a controversial act to discourage international students from staying in Australia if they cannot support themselves during the pandemic. The authors’ stories are created and retold personally for introspective and contemplative reflections on what the authors have experienced and offer considerations for how transnationality and mobility in international and comparative education could be understood through education, equity, and inclusion.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.032 | 0.023 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.002 | 0.014 |
| Research integrity | 0.003 | 0.012 |
| Insufficient payload (model declined to judge) | 0.006 | 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".