Rethinking the ownership of English as a lingua franca: sensitisation of contemporary English for Japanese university students
Bibliographic record
Abstract
椙山女学園大学研究論集 第 42 号(人文科学篇)2011 for members of global networks to develop competence in one or more additional languages, and/or to master new ways of using languages they know already' (Block and Cameron 2002: 1-2) .This development is the offspring of globalization, and the acquisition of multiple languages has become indispensable for 'members of global networks'.This also means that as members of the global community, the adoption of a common language is becoming a sine qua non for maintaining a fluent multicultural discourse.As a result of this need, and for historical, political and economical reasons, English has become one of the strongest common languages for international communication. The Differentiation between Native and Non-native Speakers of English in Japanese ELTAlthough English has been considered as an international language in the world, it seems that Japanese ELT still differentiates English as a native language (ENL) from English as a second language (ESL) and an ELF.For example, English textbooks published in Japan for senior high schools show this distinction.Here is an extract from one of the textbooks.Do you think English is the language only of people in the United States, Britain, Canada, Australia, and New Zealand?Wrong!Actually, many other people use English all over the world every day.(English 21 1997: 6, my emphasis)This extract contrasts the functions of English in terms of its different contexts.Firstly, English is a language for native speakers' intranational communication.Secondly, it is an international lingua franca for members of the global community.In the latter case, interestingly, the verb 'use' is applied.This means that ELF is conceptualised as a tool for a particular purpose, needs and advantage.What is the problem when English is conceptualised as a tool?An implication can be emerged from the extract below; English is spoken by two billion people, and only three hundred million of them speak it as their native language.Over one billion non-native speakers use English as a second language in India, Pakistan, Bangladesh, the Philippines, Nigeria, Kenya, and so on.About seven hundred million people in China, Japan, Germany, Norway, Italy . . . in fact, too many to list . . .use it for communication with the outside world.Millions more are studying it.
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 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 | 0.008 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.029 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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.
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".