Perspectives: To Challenge the Unchallenged: Potential of Non-“Standard” Englishes for Japanese EFL Learners
Bibliographic record
Abstract
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。
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".