A comparative study of English vowel shifts and vowel space area among Korean Americans in three dialect regions
Bibliographic record
Abstract
Abstract Recent sociophonetic research has focused on the ways in which race and ethnicity influence language as well as how language is used to construct racial and ethnic identity. Comparisons of the speech of members of one ethnic group across different regions are still uncommon. In this study, fifty-one native American English speakers of Korean descent, hailing from three different dialect areas of the United States (Los Angeles County and Orange County, California; Harris County, Texas; and Gwinnett County, Georgia), were recorded speaking English in casual interviews. Their speech was analyzed for characteristics of local sound patterns in each region, including the Short Front Vowel Shift (California Vowel Shift) and the Southern Vowel Shift, as well as overall Vowel Space Area. All three groups showed evidence of the Short Front Vowel Shift, and none demonstrated the Southern Vowel Shift. The Californian speakers had the smallest vowel spaces, while the Georgian speakers had the largest. We relate these findings to the ways Korean Americans in Texas and California understand their ethnic identity vis-à-vis a kind of metropolitan or urban speech style in a highly multicultural environment, while, in comparison, Korean Americans in Georgia may use vowel space to highlight their orientation toward or away from local mainstream (white) cultural identity.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".