Scrutinizing the role of length of residence and age of acquisition in the interlanguage pronunciation development of English /ɹ/ by late Japanese bilinguals
Bibliographic record
Abstract
The current project examined whether and to what degree continued L2 input, operationalized as length of residence (LOR), and age of acquisition (AOA), defined as the first intensive exposure to the target language, can be predictive of adult Japanese learners’ production of word-initial English /ɹ/. Data were collected from 65 participants, consisting of three groups of Japanese learners of English (n = 13 for Short-, Mid-, and Long-LOR groups, respectively) and two groups of baseline speakers (n = 13 for Japanese- and English-Baseline groups, respectively). Their production of /ɹ/ was elicited via three oral tasks (i.e., word reading, sentence reading, timed picture description). Acoustic analyses were carried out along four dimensions: third formant (F3), second formant (F2), first formant (F1) frequencies, and formant transition duration. The results demonstrated that (a) all learners reached native-like proficiency with respect to the use of existing cues (F2, transition duration) within approximately one year of LOR, (b) their performance was negatively related to AOA to some degree, and (c) longer LOR was predictive of the development of the new cue (F3). These results suggest that late L2 speech sound acquisition and proficiency may be characterized by different levels of phonetic processing.
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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 | 0.001 | 0.001 |
| 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 | 0.001 | 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".