Haunting Native Speakerism? Students’ Perceptions toward Native Speaking English Teachers
Bibliographic record
Abstract
This paper intends to explore how Taiwanese university students perceive their native-speaking English teachers (NESTs). Mutual expectations between the NESTs and students are also investigated. Collected data include questionnaires from 107 students and interviews with three NESTs and 19 students who have filled out the questionnaire. The result shows that students expect more encouragement and interaction with the NESTs, and more relaxed activities with less assignment and test. A third of the students expect NEST with a standard accent, while a quarter do not care about accent at all. The NESTs reveal their dissatisfaction toward the students’ passiveness and irresponsiveness. While students expect their NESTs to be interactive, they themselves seem to give the NESTs an impression of an unwillingness to participate. The discussion centers on this dilemma and offer some suggestions for English teachers.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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 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".