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Record W7035747543

The aftermath of the pandemic: exploring transnational identity development among Chinese international students in Canada

2023· dissertation· en· W7035747543 on OpenAlexaboutno aff

Bibliographic record

VenueMspace (University of Manitoba) · 2023
Typedissertation
Languageen
FieldEngineering
TopicRFID technology advancements
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionTSG101Circumstantial evidenceContext (archaeology)HyporeflexiaFrugality
DOInot available

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.932
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.211
Teacher spread0.198 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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