The Effect of Ethnicity on Identification of Korean American Speech
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 on ethnic varieties of American English has found that listeners can identify speaker ethnicity from voice alone at above-chance rates. This study aims to extend this research by focusing on the perception of race and ethnicity in the voices of ethnically Korean speakers of English. Bilingual Korean Americans in California provided samples of English speech that were rated by 105 listeners. Listeners rated the speakers on their likelihood of being a certain race or ethnicity (including Asian and White). Listeners who were Korean themselves rated the speakers as more likely to be Asian and Korean, whereas non-Asian listeners rated the speakers as more likely to be White. Non-Asian listeners also demonstrated a negative correlation between rating a voice as Asian and rating a voice as belonging to a native-born American, while Asian listeners did not. Finally, a positive correlation between pitch and perceived Asianness was found for female speakers, corresponding to listeners’ metalinguistic commentary about the hallmarks and stereotypes of Asian or Asian American speech. The findings implicate the listener’s own ethnic identity and familiarity with a speech variety as an important factor in sociolinguistic perception.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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