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Record W3004293111 · doi:10.5539/hes.v10n2p1

Identity Negotiation in Chinese University English Classroom

2020· article· en· W3004293111 on OpenAlexvenueno aff
Zhou Nan

Bibliographic record

VenueHigher Education Studies · 2020
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsnot available
Fundersnot available
KeywordsConstruct (python library)Identity (music)NegotiationClass (philosophy)Identity negotiationPsychologyPedagogyIntercultural communicationMathematics educationSociologyComputer science

Abstract

fetched live from OpenAlex

Through a theoretical framework that builds on the Community of Practice construct and the concepts of identity negotiation, imagined identity and investment, this case study examines how one English-as-a-foreign-language student negotiated the identity as an English learner in the Chinese university classroom setting. Then the extent that the student’s oral communication behaviors in the English classroom community were influenced by the negotiated identity is presented. The analysis shows that a student may have multiple identities in the educational setting. By constantly shifting identities in the English learning process, the focal student struggled with the English learner identity perceived by herself and that identified by her English teacher. As a result, the student’s investment in English class oral tasks and communication behaviors in EFL classroom may change with the shift of identities. Suggestions are made for EFL teachers to help students construct desirable identities in order to improve their involvement in English class oral communication activities.

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)0.006
metaresearch head score (Gemma)0.007
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 score0.021
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0210.012
Scholarly communication0.0080.005
Open science0.0020.009
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.066
GPT teacher head0.308
Teacher spread0.242 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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