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Record W3213599449 · doi:10.1080/01434632.2021.1996582

Chinese dual-language bilingual education teachers’ pedagogical and languaging practices in American immersion schools

2021· article· en· W3213599449 on OpenAlexaff
Wenying Zhou, Guofang Li

Bibliographic record

VenueJournal of Multilingual and Multicultural Development · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicMultilingual Education and Policy
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsTranslanguagingBilingual educationPedagogyDual languagePsychologyMathematics educationCurriculumFocus on formNeuroscience of multilingualismFaculty developmentChinese languageSecond languageProfessional developmentLinguistics

Abstract

fetched live from OpenAlex

Based on video recordings of five pre-k to grade 4 Chinese dual-language bilingual education (DLBE) teachers’ classroom instruction, this article examines the Chinese DLBE teachers’ target and first language use and pedagogical moves during their processes of learning to teach in immersion schools in the US. Conversational analyses of classroom interactions show that the teachers’ subscribed to target language (TL) only instruction with minimum translanguaging and that teacher-fronted talk in the TL dominated their teaching with low student language output. Although their attention to curriculum-related content varied depending on lesson focus and student TL proficiency, a significant amount of teacher languaging practices was for classroom management. The findings have important implications for Chinese DLBE teachers’ preparation and professional development.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.051
Threshold uncertainty score0.101

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.076
GPT teacher head0.498
Teacher spread0.421 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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