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Record W2107019368 · doi:10.2307/3588523

Negotiating Language Contact and Identity Change in Developing Tibetan-English Bilingualism

2005· article· en· W2107019368 on OpenAlexaff
Seonaigh MacPherson

Bibliographic record

VenueTESOL Quarterly · 2005
Typearticle
Languageen
FieldArts and Humanities
TopicSecond Language Learning and Teaching
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsNeuroscience of multilingualismLanguage contactIdentity (music)LinguisticsNegotiationSociologyLanguage changePsychologySocial scienceArtPhilosophy

Abstract

fetched live from OpenAlex

This article explores the identity struggles of a community of Tibetan refugee women in the Indian Himalayas whose educational program combines a traditional Buddhist philosophical curriculum in Tibetan alongside a modern, secular bilingual curriculum in English-Tibetan. Ethnographic and action research data illustrate how negotiations of meanings in multicultural, multilingual EFL/EIL contexts go well beyond mere linguistic features to include cultural and gender identity struggles. Five students serve as case studies to consider five alternative patterns of identity and language negotiations: rejection, assimilation, marginality, bicultural accommodation, and intercultural creativity. The author relates these cross-cultural identity negotiations to various gender identity stances. She concludes by recommending a program of inter-cultural language teaching to address the increasingly global context of English language teaching and learning.

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)0.003
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 score0.049
Threshold uncertainty score0.098

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0190.011
Scholarly communication0.0070.002
Open science0.0010.009
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.029
GPT teacher head0.284
Teacher spread0.255 · 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

Citations35
Published2005
Admission routes1
Has abstractyes

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