The aftermath of the pandemic: exploring transnational identity development among Chinese international students in Canada
Bibliographic record
Abstract
The COVID-19 pandemic has prompted a significant shift in the focus of international student migration (ISM) literature towards understanding and addressing the profound impact of the pandemic on international students. However, the existing literature primarily centers on their lived experiences within the host country, often overlooking the current reality of these students whose lives may transcend traditional geographical borders of nation-states. Despite the transnational nature of their lives enabled by accessible modes of transportation and the rapid development of online media technologies, this aspect remains inadequately acknowledged. To address this gap, adopting a transnational lens that scrutinizes localities in more than one nation-state and replaces dichotomies (home countries versus host countries) with the notions of fluidity can be helpful to illuminate a more nuanced and richer account (Nowicka, 2020; Toukan et al., 2020). Using life history interviews as the primary research method and a transnational lens as the theory, this paper aims to examine 1) the reported lived experiences of Chinese international students in Canada, especially during the pandemic period, and 2) how these lived experiences affect them in terms of their developing transnational identities? The results shed light on the struggles, challenges, and personal growth encountered by Chinese international students within the context of the pandemic, highlighting the complex, dynamic, and in-becoming nature of their transnational identity.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.020 | 0.008 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".