L2 English Speakers’ Perception of Their English Accent: An Investigation of European and Asian Attitudes
Bibliographic record
Abstract
According to previous studies, Japanese learners of English (JLEs) have a negative perception of their own variety of English along with a strong desire to sound native-like. Language attitudes toward L2 (second language) English accents may affect their active participation in English communication situations. The present study is cross-national and investigates whether other L2 English learners from different L1 (first language) backgrounds negatively perceive their own variety of English and English pronunciation as JLEs do. A total of 290 college students in Austria, Germany, Denmark, Malaysia, China, Japan, and Kazakhstan evaluated their own accent by responding to 10 statements on a 6-point scale. By comparing the responses as percentages and the binomial test, the analysis revealed that the Japanese perceived their accent most negatively, followed by the Chinese, whereas the Europeans, Malaysians, and Kazakhs perceived their accents positively to varying degrees. Among the seven countries, the L1 Danish group perceived their own variety as native-like most and non-native accent least, where the JLEs showed the opposite results. On the other hand, the endorsement for native accent was recognized across the countries. Based on the results, I claim that individual socio-contextual settings could have a critical impact on developing distinct attitudes toward one’s own accent among EFL speakers.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".