Assessing L1 Mandarin and L2 English influence on the L3 production of French obstruent coda voicing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Previous research on crosslinguistic influence in third language (L3) phonetic and phonological production has found that both the first language (L1) and the second language (L2) are possible sources of influence. Such research, however, has mainly examined language triads involving European languages and structures (e.g. vowels, VOT) shared by all three languages. In the present study, we seek to expand the empirical basis for assessing the relative contribution of learners’ L1 and L2 via a study of L1-Mandarin–L2-English–L3-French learners’ production of obstruent coda voicing, a feature and structure lacking in their L1. Two hypotheses are tested: that greater influence will come from the learners’ L2 English, and that such influence will be most common among learners of high L2, low L3 oral proficiency as measured by an accentedness task. Participants completed a carrier sentence reading task involving nonce words. We analysed two phonetic parameters: vowel–consonant (VC) duration ratio and percentage (%) obstruent voicing. Considerable support was found for primarily L2-based influence. However, the data did not support any effect of L2 or L3 proficiency. This study is one of the first to examine the production of multiple phonetic parameters for a single phonological contrast. Our results provide new evidence for L2 facilitative transfer, especially when the L1 has a different syllable structure from the L2 or L3. They also reveal that L2-based transfer may affect some phonetic cues but not others. In the present instance, in a subset of speakers we found evidence of transfer affecting only one of the two phonetic cues (the VC duration ratio or % obstruent voicing). These findings further illuminate the complex nature of L3 acquisition.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it