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Record W360612826 · doi:10.37546/jaltjj31.2-6

Perspectives: To Challenge the Unchallenged: Potential of Non-“Standard” Englishes for Japanese EFL Learners

2009· article· en· W360612826 on OpenAlexaff
Kazufumi Miyagi, Masatoshi Sato, Alison Crump

Bibliographic record

VenueJALT Journal · 2009
Typearticle
Languageen
FieldArts and Humanities
TopicEFL/ESL Teaching and Learning
Canadian institutionsMcGill University
Fundersnot available
KeywordsLinguisticsEnglish languageWorld EnglishesInclusion (mineral)Standard EnglishPsychologyLanguage proficiencySociologyMathematics educationPhilosophySocial psychology

Abstract

fetched live from OpenAlex

This paper calls for a broadening of the discussion of English language teaching (ELT) practices in Japan. We review issues associated with the global spread of English and link this discussion to the present “standard” English model of ELT in Japan. We propose three major benefits that would follow from an inclusion of non-“standard” (i.e., non American/British) Englishes in Japanese EFL classrooms. First, familiarity with different varieties could increase learners’ confidence when interacting with other non-native speakers (NNSs). Second, we review literature that shows that NNS-NNS interactions actually help learners improve their language skills. Finally, recognition of non-“standard” varieties of English would help Japanese learners challenge monolithic western-centric worldviews that marginalize regional, cultural, and linguistic norms and values. We connect this theory to practice by suggesting some possible changes to ELT in Japan. 本稿では、英語・米語に代表されるいわゆる標準英語の社会的文化的な影響について指摘し、日本英語教育において標準英語に対抗すべく多様な「非標準」英語の教育的可能性を探るものである。著者それぞれの研究を踏まえ、英米語に加え「非標準」英語を日本の英語教育現場で積極的に活用することで期待できる利点を三つ提唱する。第一に「非標準」英語に親しみを持つことにより、ノンネイティブ話者同士の対話に自信が持てるようになる。第二にノンネイティブ話者同士による対話活動は実際に第二言語習得に効果的である。第三に、「非標準」英語に触れることが、西洋的視点に偏りがちな日本人の世界観を省みる機会となり、多様な文化、言語に対する認識の向上が期待できる。以上の点を考察した上で、最後に英語教育現場における「非」標準英語の具体的な導入法ついて提案する。

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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.237
Threshold uncertainty score0.745

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 integrity0.0000.001
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 head0.025
GPT teacher head0.278
Teacher spread0.253 · 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 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

Citations7
Published2009
Admission routes1
Has abstractyes

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