How L1-Chinese L2-English learners perceive English front vowels: A phonological account
Bibliographic record
Abstract
Second language acquisition involves readjusting features from one’s L1 onto counterparts in the L2. Learners often face difficulty during this process due to the presence of an already firmly rooted L1 grammar. Furthermore, a learner’s L1 serves to constrain sensitivity to non-native contrasts during the acquisition process. If a learner’s L2 grammar lacks the phonological feature that can differentiate a non-native contrast, then that learner may experience persistent difficulties in representing the L2 sounds as a result. Mandarin learners of English as a second language have to contend with a substantially expanded L2 vowel inventory in the early stages of acquisition, grappling with the addition of pronounced features less prevalent in their L1. In an attempt to account for front vowel acquisition difficulties and possible routes to progress for L1- Mandarin L2-English using a direct transfer approach, this work follows the Toronto School of contrastive phonology which holds that phonological representation is determined primarily through the ordering of contrastive features. We present data from recent phonetic research that catalogues Mandarin learners’ progress in incorporating English front vowels while, at the same time, examining the underlying phonological processes. This serves as the basis for a preliminary model of contrastive hierarchy in language acquisition using elements of a feature geometry paradigm. The model provides a theoretical roadmap showing that, as Mandarin learners progress and gradually incorporate English front vowels into their L2 repository, the learner’s L2 hierarchy evolves through successive stages as contrasts are perceived and categorized.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".