L1 phonological effects on L2 (non-)naïve perception: A cross-language investigation of the oral–nasal vowel contrast in Brazilian Portuguese
Bibliographic record
Abstract
Feature-based approaches to acquisition principally focus on second language (L2) learners’ ability to perceive non-native consonants when the features required are either contrastively present or entirely absent from the first language (L1) grammar. As features may function contrastively or allophonically in the consonant and/or vowel systems of a language, we expand the scope of this research to address whether features that function contrastively in the L1 vowel system can be recombined to yield new vowels in the L2; whether features that play a contrastive role in the L1 consonant system can be reassigned to build new vowels in the L2; and whether L1 allophonic features can be ‘elevated’ to contrastive status in the L2. We examine perception of the oral–nasal contrast in Brazilian Portuguese listeners from French, English, Caribbean Spanish, and non-Caribbean Spanish backgrounds, languages that differ in the status assigned to [nasal] in their vowel systems. An AXB discrimination task revealed that, although all language groups succeeded in perceiving the non-naïve contrast /e/–/ẽ/ due to their previous exposure to Québec French while living in Montréal, Canada, only French and Caribbean Spanish speakers succeeded in discriminating the naïve contrast /i/–/ĩ/. These findings suggest that feature redeployment at first exposure is only possible if the feature is contrastive in the L1 vowel system (French) or if the feature is allophonic but variably occurs in contrastive contexts in the L1 vowel system (Caribbean Spanish). With more exposure to a non-native contrast, however, feature redeployment from consonant to vowel systems was also supported, as was the possibility that allophonic features may be elevated to contrastive status in the L2.
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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 | 0.002 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".