Linguistic correlates of second language users’ attitudes to Arabic and Chinese varieties of English: a verbal guise study
Bibliographic record
Abstract
Research into language attitudes suggests that L2 users often hold negative attitudes toward their own and others’ L2 accents. However, less is known about specific features that affect these attitudes. Beinhoff’s study explored consonantal variation and its impact on perceptions of L2 speakers, but this study further examines linguistic correlates of L2 users’ attitudes toward Arabic and Chinese varieties of English. Using the verbal guise method, Arabic and Chinese male and female speakers read a paragraph in English with varying L1 influences. Each sample was rated by 30 L2 listeners on a 6-point semantic differential scale assessing status, solidarity, and dynamism. Phonological and fluency analyses of the samples revealed that non-segmental features, such as prosody, play a more significant role in eliciting positive attitudes toward these English varieties than do segmental features. These findings highlight the importance of suprasegmental aspects in shaping listener perceptions of L2 English speech.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".