Cross-linguistic Phonological Transfer:
Bibliographic record
Abstract
The perception and acquisition of non-native tense and lax vowel contrasts have been the subject of extensive research (Bustos et al., 2023; Chang, 2023; Fabra & Romero, 2012; Lai, 2010). Previous studies have highlighted various factors influencing the perception of these contrasts, such as linguistic background, exposure to the target language, and individual phonetic training (Casillas, 2015; Souza et al., 2017; Chang & Weng, 2012). However, there has been limited investigation into whether speakers can transfer discrimination abilities from the vowel contrasts in their first language (L1) to novel contrasts in a second language (L2) that differ in specific phonetic features. Focusing on this inquiry, the present research examines whether native speakers of Bangla, a language with tense/lax contrasts limited to mid vowels, can extrapolate this ability to discriminate tense/lax contrasts among high vowels in English, a language with tense/lax contrasts among both mid and high vowels. Through a forced-choice identification task involving English minimal pairs, data were collected from 43 adult Bangla speakers who had learned L2 English. Contrary to expectations, results indicated that these speakers were unable to effectively distinguish between tense and lax high vowels in English, suggesting that the presence of a similar contrast in L1 does not necessarily facilitate the acquisition of comparable distinctions in L2 across different vowel groups. Implications of the results for non-native vowel acquisition and the pedagogy of English language teaching to Bangla speakers are discussed.
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How this classification was reachedexpand
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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".