Temporal patterns of native Mandarin Chinese speakers’ productions of English stop-vowel syllable
Bibliographic record
Abstract
Second language (L2) production can be a kind of interlanguage, a relatively stable system bearing the nature of both the native language (L1) and L2. Within such a system sound components of a syllable may bear their own interlanguage characteristics and yet interact with the other component sounds. The present study investigates temporal patterns of L1–L2 interaction at the syllable level. Audio recordings were made of English stop-vowel syllables produced by native speakers of Mandarin who were fluent in English (ChE). Native English productions (AmE) of these syllables and native productions of Mandarin (ChM) stop-vowel syllables were acquired as native norms. Temporal measures included stop closure duration, voice-onset time (VOT), vowel duration, and syllable duration. Results show that the internal timing components of ChE often deviate from AmE, with the closure duration, VOT, and vowel duration being intermediate to AmE and ChM. However, at the syllable level, ChE productions tend to follow the overall patterns of AmE. Temporal deviations were often compensated by temporal compensation of other components in the syllable, maintaining a balanced consonant/vowel distribution. These findings have implications for a broader understanding of L2 productions.
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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 | 0.001 |
| 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 | 0.000 | 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".