A Phonetic Account of Spanish-English Bilinguals’ Divergence with Agreement
Bibliographic record
Abstract
Does bilingual language influence in the domain of phonetics impact the morphosyntactic domain? Spanish gender is encoded by word-final, unstressed vowels (/a e o/), which may diphthongize in word-boundary vowel sequences. English neutralizes unstressed final vowels and separates across-word vocalic sequences. The realization of gender vowels as schwa, due to cross-linguistic influence, may remain undetected if not directly analyzed. To explore the potential over-reporting of gender accuracy, we conducted parallel phonetic and morphosyntactic analyses of read and semi-spontaneous speech produced by 11 Monolingual speakers and 13 Early and 13 Late Spanish-English bilinguals. F1 and F2 values were extracted at five points for all word-final unstressed vowels and vowel sequences. All determiner phrases (DPs) from narratives were coded for morphological and contextual parameters. Early bilinguals exhibited clear patterns of vowel centralization and higher rates of hiatuses than the other groups. However, the morphological analysis yielded very few errors. A follow-up integrated analysis revealed that /a and o/ were realized as centralized vowels, particularly with [+Animate] nouns. We propose that bilinguals’ schwa-like realizations can be over-interpreted as target Spanish vowels. Such variable vowel realization may be a factor in the vulnerability to attrition in gender marking in Spanish as a heritage language.
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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 | 0.001 | 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) | 0.004 | 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".