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Record W4368374786 · doi:10.1177/09593543221130728

The politics of Chinese immigrants’ double unbelonging and deglobalization

2023· article· en· W4368374786 on OpenAlexfundno aff
Zhipeng Gao

Bibliographic record

VenueTheory & Psychology · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicMigration, Refugees, and Integration
Canadian institutionsnot available
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGeopoliticsDialecticSociologyPoliticsImmigrationChinaGender studiesGlobalizationAcculturationChinese americansPositivismState (computer science)PostmodernismPolitical scienceEpistemologyLaw

Abstract

fetched live from OpenAlex

This study theorizes the politics of belonging, drawing on the case of Chinese immigrants. In the heyday of globalization, Chinese immigrants used to enjoy a high degree of transnational mobility and multiple belongings. Now, in the wake of China–West geopolitical contestations and during the time of COVID-19, many Chinese immigrants are experiencing double unbelonging due to marginalization in both the host society and China. By analyzing double unbelonging, this study makes three theoretical contributions. First, it expands the conventional cultural–humanistic framework of belonging to incorporate political analysis. Second, it discusses why and how to replace the positivist approach to belonging as exemplified by acculturation theory with a social constructionist approach to the politics of belonging. Finally, the study theorizes unbelonging—its epistemological advantage, its dialectical relation with belonging, its production by the nation-state and media, and how polarizing geopolitics produce double unbelonging.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.111
Threshold uncertainty score0.415

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.018
GPT teacher head0.364
Teacher spread0.346 · 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 designTheoretical or conceptual
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

Citations7
Published2023
Admission routes1
Has abstractyes

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