Voice onset time of bilingual English and French-speaking Canadians
Bibliographic record
Abstract
There is evidence of voice onset drift when bilinguals are submerged in cultures that are dominated by their second language (L2). Sancier and Fowler [Gestural drift in a bilingual speaker of Brazilian Portuguese and English,‘‘ J. Phonet. 4, 421-436 (1997)] found that voiceless stop voice onset times (VOTs) of a bilingual speaker of Brazilian Portuguese and English were longer after months spent in the US than after time spent in the speaker’s first language (L1) context. Flege [’’The production of ‘‘new’’ and ‘‘similar’’ phones in a foreign language: Evidence for the effect of equivalence classification,‘‘ J. Phonet. 15, 47–65 (1987)] studied French L1 English L2 bilinguals and English L1 French L2 bilinguals who had spent about 12 years living in the L2 dominant culture. The French L1 English L2 bilinguals VOTs were longer than the VOTs of French monolinguals. The English L1 French L2 bilinguals VOTs were shorter than the VOTs of English monolinguals. In the present study the nature of the experience underlying VOT drift was investigated. Specifically, the aim of the current study was to see whether exposure to another language with different VOTs affects VOT production in the first (or only) language or if VOT production drift is dependent on speaking the non-native language. Productions from English and French monolinguals and from English-French bilinguals were compared across varying linguistic contexts and order of acquisition.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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".