Revisiting Effects of Native Speakerism on Thai Teachers and Students in the Age of English as a Lingua Franca
Bibliographic record
Abstract
Currently, the role of English language has changed from being a language used among native English speakers (NESs) to being a language spoken by people of various backgrounds or known as English as a lingua franca (ELF). This phenomenon has affected different aspects of global English usage and users across the world. However, in Thailand, this issue does not seem to be taken into account with regard to English language teaching (ELT) and learning practices as different ELT stakeholders continue to conform to traditional teaching methods related to NESs. This action is reflected in an English language ideology called ‘native speakerism’ which has long been entrenched in Thai society. It has repeatedly caused different problems for both Thai teachers and students of English language until the present day. This review article aims to demonstrate the impacts of the native speakerism ideology on Thai teachers and students of English language in the period of English as a global lingua franca. The article begins with an explanation of how the changes of role and status of English challenge traditional perspectives of English language and how the ELT industry around the world, including in Thailand, should adapt to such changes. Then, the article gives brief conceptualizations of native speakerism and its effects on English teachers and students. Finally, it moves on to discuss the native speakerism ideology in Thailand and reports different negative effects of native speakerism on Thai teachers and students of English language.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".