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Record W3046268738 · doi:10.1177/0117196820935995

Conceptualizing virtual transnational diaspora: Returning to the ‘return’ of Chinese transnational academics

2020· article· en· W3046268738 on OpenAlexaff
Lei Ling, Shibao Guo

Bibliographic record

VenueAsian and Pacific migration journal · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicDiaspora, migration, transnational identity
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsDiasporaNegotiationSociologyChinaBeijingTransnationalismGender studiesPolitical scienceSocial sciencePolitics

Abstract

fetched live from OpenAlex

Transnational migration brings to the fore the various social and professional connections migrants maintain with their home and sojourn countries. Drawing on a qualitative case study with 12 Chinese transnational academics in the field of the social sciences and humanities in three higher education institutions in Beijing, China, this article explores their transnational ways of being and belonging. Informed by the theoretical lens of transnational diaspora, our study indicates that the concept of “returnee” is too restricted to capture the transnational work and learning practices and the self-identification of Chinese transnational academics. Our analysis reveals that the study-abroad experience as a PhD student shapes the multiple and simultaneous ways of being and ways of belonging of the transnational academics in relation to China, the host countries where they pursued doctoral studies and, increasingly, de-territorialized transnational academic communities. Mobilizing digital communication technologies, they create spaces to negotiate their identities as researchers, ethnic Chinese and members of transnational academic communities. Their work and learning in transnational spaces have contributed to the formation of virtual transnational diaspora characterized by the inter-dependence of academics across borders.

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)0.001
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: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
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 head0.033
GPT teacher head0.293
Teacher spread0.260 · 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 designQualitative
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

Citations26
Published2020
Admission routes1
Has abstractyes

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