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Record W2095182364 · doi:10.1080/14767724.2014.934071

Becoming transnational: exploring multiple identities of students in a Mandarin–English bilingual programme in Canada

2014· article· en· W2095182364 on OpenAlexaffabout
Zhang Yan, Yan Guo

Bibliographic record

VenueGlobalisation Societies and Education · 2014
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTransculturationMandarin ChineseTransnationalismEthnic groupIdentity (music)SociologyLinguisticsProcess (computing)Gender studiesPolitical scienceComputer scienceAnthropologyPoliticsAestheticsArt

Abstract

fetched live from OpenAlex

Guided by post-structural perspectives of identities as processes of becoming and transculturation and transnationalism, this study explores how multilingual students in a Mandarin–English bilingual programme form their sense of identities in a dynamic process. Multiple forms of data are collected, including observations, interviews and documents. The findings indicate that multilingual students are mobile, namely, they move across linguistic, cultural and ethnic spaces of interaction. In addition, they challenge the dominant discourse of any fixed and hyphenated identity and take up transcultural and transnational identities that allow their comfortable circulation among different worlds. This study calls for a need to unfold children's multiple and mobile identities and explores new possibilities for life.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score0.265

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0360.014
Scholarly communication0.0100.003
Open science0.0020.010
Research integrity0.0010.005
Insufficient payload (model declined to judge)0.0030.000

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 head0.081
GPT teacher head0.390
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), 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

Citations24
Published2014
Admission routes2
Has abstractyes

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