Release bursts in English word-final stops: A longitudinal study of Korean adults’ and children’s production
Bibliographic record
Abstract
Stops at the end of Korean words are always unreleased. The question addressed here was whether Korean adults and children living in the U.S. can learn to release stops at the end of English words. Four groups of 18 native Koreans (NK) who differed according to age (adult versus child) and length of residence in the U.S. (3 vs 5 years at T1) participated. Two native English (NE) groups served as age-matched controls. Production data were collected at two times (T1, T2) separated by one year. English words ending in /t/ and /k/ were then examined in perception experiments (Exp. 1, Exp. 2). NE-speaking judges decided whether the final stop has a release burst or not. Exp. 1 showed that NE talkers released /t/ more often than NK talkers did. The effect of time was also significant. Talkers produced release bursts more often at T2 than at T1. Exp. 2 showed that, unlike Exp. 1, there were significant differences between NK adults and children. While NK children did not differ from NE children, NK adults released the final /k/ much less often than NE adults did. Possible reasons for why the expected children’s advantage was seen for /k/, but not for /t/, will be discussed. [Work supported by NIH.]
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".