Different Englishes, different resources of contextualization: from the perspective of the ecology of communication
Bibliographic record
Abstract
of Communication lppeiiNOUE l.lntroductionEnglish is unquestionably a dominant international language in today's world.The expansion of English speaking communities and the varieties of the language produce several intriguing phenomena from linguistic and sociolinguistic points of view.Quite a few researches and observations have been conducted on WorldEnglishes; hence, the plural form of English is accepted to be common and is now well-establishedin society-and variety-oriented fields of language study.World Englishes, as an academic paradigm, aims to describe non-native English throughout the world as discrete varieties rather than as "non-standard" English.What varies, however, is not only the formal aspects of the language, such as pronunciation and vocabulary, but also communicative conventions, which are utilized as resources of contextualization (Gumperz 1982;Inoue 2003 Inoue , 2004 Inoue , 2005)).Using several examples ofWorldEnglishes, this paper aims to demonstrate some of the contextualizing processes wherein variety-specific resources are utilized for communicating messages and signaling ethnocultural symbolic values from an ecological perspective on sociolinguistic phenomena, which I would call (188) -409-the ecology of communication.Kachru (1985Kachru ( , 1992) ) proposed a three circle model that describes the situation of today's World Englishes.One is the Inner Circle, which refers to those countries wherein virtually all public and private verbal interaction is carried out in English among majority of the population, such as Australia, Canada, the UK, the United States, etc. Another is the Outer Circle, which indicates the countries wherein private interaction normally does not take place in English among majority of the population, but the people publicly interact in English for legal, political, educational, and other official matters; these countries include.Ghana, India, Malaysia, Nigeria, Singapore, etc.Yet another circle in this model is the Expanding Circle, which is characterized by the countries wherein English is a "foreign" language and is taught as a subject at school; countries in this circle include China, Japan, Korea, Saudi Arabia, Zimbabwe, etc.This model is considered to be the basis for the study of World Englishes, and quite successfully deals with the types of the spread, acquisition, and function of English in various socio-cultural contexts.Many of the investigations in this field so far have focused on macro-analysis of English varieties in terms of regional differences (
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".