Attitudinal Identification: An Essential Paradigm for the Growth of Non-native Varieties
Bibliographic record
Abstract
The significance of English as a ‘Link language’ and as a ‘Global Language’ has increased in the past few decades. English is seen as ‘a language of career’ and ‘a passport for success’ in different walks of life. Active steps have been taken to impart knowledge of English in the ‘Outer Circle’. In the outer circle, English is used as a Second language and it includes countries like India, Singapore, Pakistan and Africa. While some research has been conducted on these varieties, much exhaustive work still needs to be done from the perspective of Standardizing these varieties. The present study sheds light on the attitudes of teachers, learners, curriculum designers, the corporate world and the decision makers in India towards the language variety they use. Though Indian English is considered to have achieved recognition and prestige in the past few years, it is observed that most of the Indian speakers of English still do not take ‘pride’ in asserting the fact that the variety of English that they speak is ‘Indian English’, and also quite often label the variety that they speak as either ‘British English’ or ‘American English’. It is felt that ‘Attitudes’ play a significant role in the growth or decay of a particular language variety. The study argues that the Attitudinal Identification with the variety that we speak is an essential paradigm for the growth of a language variety. The study cites some examples from different non-native varieties and it suggests some strategies that could be adopted so as to bring a shift in the people’s attitude.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.035 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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".