The Politics of Standard English: An Exploration of Thai Tertiary English Learners’ Perceptions of the Notion of Standard English
Bibliographic record
Abstract
Given that English becomes a lingua franca in the world in which more and more non-native speakers use it to suit their own purposes in local contexts, the ownership of English becomes denationalized. English as an international language scholars have maintained that English learners do not need to approximate the norms of native speakers as closely as possible. Hence, pedagogical attempts based on native-speaker linguistic standards become irrelevant in the contexts where English is mainly used as a lingua franca to serve such wider communicative purposes. In this study, I investigated how the notion of standard English was construed by the Thai tertiary English majors. Focus group interview was used as a research tool to obtain participants’ critical perceptions. The results revealed that although the participants expressed that the notion of standard English was a complex issue that requires careful interpretation, deeply inside, it was still anchored to the ideology of native speaker or at least had to include the construct of native speaker in its working definition. Maintaining that the notion of standard English is a political construct rather than a linguistic reality, the paper ends by suggesting some pedagogical ideas that aim at raising learners’ awareness of the global role of English.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.004 |
| 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".