Linguistic correlates of second language users’ attitudes to Arabic and Chinese varieties of English: a verbal guise study
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it