Bibliographic record
Abstract
For heritage speakers (HSs), expectations of influence from the community’s dominant language are pervasive. An alternative account for heritage language variability is that HSs are demonstrating sociolinguistic competence: HSs may either initiate or carry forward a pattern of variation from the homeland variety. We illustrate the importance of this consideration, querying whether /u/-fronting in Heritage Korean is best interpreted as influence from Toronto English, where /u/-fronting also occurs, or a continuation of an ongoing vowel shift in Homeland (Seoul) Korean that also involves /ɨ/-fronting and /o/-fronting. How can patterns of social embedding untangle this question that is central to better understanding sociolinguistic competence in HSs? For Korean vowels produced in sociolinguistic interviews by Heritage (8 adult immigrants, 8 adult children of immigrants) and 10 Homeland adults, F1 and F2 were measured (13,232 tokens of /o/, 6810 tokens of /u/, and 20,637 tokens of /ɨ/), normalized and subjected to linear regression. Models predict effects of gender, age, orientation toward Korean language and culture, the speaker’s average F2 for the other shifting vowels, and duration. These models highlight HS’s sociolinguistic competence: Heritage speakers share linguistic and social patterns with Homeland Korean speakers that are absent in English. Additionally, heritage speakers lack the effects of factors attested in the English change.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".