Accent Priority in a Thai University Context: A Common Sense Revisited
Bibliographic record
Abstract
In Thailand, there has been much debate regarding what accents should be prioritized and adopted as models for learning and use in the context of English language education. However, it is not a debate in which the voices of English learners have sufficiently been heard. Several world Englishes scholars have maintained that being a denationalized language, English should be viewed through the lens of linguistic hybridization. In this paper, we investigated Thai university English learners’ preferences for varieties of English and their attitudes towards the importance of understanding varieties of English in order to generate a better understanding as to what extent native and non-native varieties gain acceptance as English models. We also explored whether learners’ attitudes were consistent with the ideology of English as an international language which sees English in its pluralistic sense. The findings of this study suggest that even though the majority of learners preferred native-speaker accents as models for learning and use, they consiered non-native Englishes worth understanding and learning. The findings challenge the old paradigm of English language teaching that is based on the concept of linguistic Americanization or Britishization, prioritizing the native-speaker school of thought. In closing, we proposed some pedagogical suggestions that, we believe, are consistent with how English functions in the world as an international lingua franca.
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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 | 0.017 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.011 | 0.045 |
| Scholarly communication | 0.018 | 0.016 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".