‘She Has an Accent’ - When Pronunciation Overrides Appearance in Determining Whether Someone Is a Native English Speaker
Bibliographic record
Abstract
Studies on nativeness affirm that being judged/perceived as a native/non-native English speaker is determined by social factors such as nationality, variety spoken and ethnicity. This study investigated how listeners from seven different countries judged speakers who were audio and video recorded as native or non-native English speakers by comparing whether having access to the videos made listeners change their ratings. Nine speakers from different linguistic backgrounds were audio and video recorded. Thirty-two listeners listened and watched the recordings and judged speakers as native/non-native English speakers. Listeners’ judgements were compared and analyzed as well as their comments for each speaker. The results show that though a few listeners considered appearance when rating the speakers, only a minority of them, in a minority of cases, changed their judgements when they saw the videos, and of those, few referred explicitly to appearance or geographical origin as information they used in making their judgement. Instead pronunciation emerged as the most commonly cited and consistent factor influencing listeners’ perceptions of nativeness.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 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".