Language Dominance Modulates the Perception of Spanish Approximants in Late Bilinguals
Bibliographic record
Abstract
The ability to discriminate phonetically similar first language (L1) and second language (L2) sounds has significant consequences for achieving target-like proficiency in second-language learners. This study examines the L2 perception of Spanish approximants [β, δ, ɣ] in comparison with their voiced stop counterparts [b, d, g] by adult English-Spanish bilinguals. Of interest is how perceptual effects are modulated by factors related to language dominance, including proficiency, language history, attitudes, and L1/L2 use, as measured by the Bilingual Language Profile questionnaire. Perception of target phones was assessed in adult native Spanish speakers (n = 10) and Spanish learners (n = 23) of varying proficiency levels, via (vowel-consonant-vowel) VCV sequences featuring both Spanish approximants and voiced stops during an AX discrimination task. Results indicate a significant positive correlation between perceptual accuracy and a language dominance score. Findings further demonstrate a significant hierarchy of increasing perceptual difficulty: β < δ < ɣ. Through an examination of bilingual language dominance, composed of the combined effects of language history, use, proficiency, and attitudes, the present study contributes a more nuanced and complete examination of individual variables that affect L2 perception, reaching beyond proficiency and experience alone.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".