Voice onset time (VOT) in Canadian French and English: Monolingual and bilingual adults
Bibliographic record
Abstract
This study focused on the contrasts produced by early bilingual speakers (n=6) across their two languages in comparison with monolingual speakers (Canadian English (CE), n=5; Canadian French (CF), n=6). VOT production was measured in monosyllabic CE and CF words that began with one of four stop consonants, /p, b, t, d/ followed by one of three vowels. A total of 14–18 words for each of the four stop consonants for each language was elicited with a total number 1700 acoustically analyzed productions. The participants were tested individually in quiet rooms using a single target language throughout the session. As expected, the monolingual speakers produced a two-way contrast (statistically significant: p<0.05): for CE speakers, short-lag VOT versus long-lag VOT; for CF speakers, lead VOT versus short-lag VOT. Rather than producing a two-way contrast (e.g., lead VOT versus lag VOT) or a three-way contrast (e.g., lead VOT versus short-lag VOT versus long-lag VOT), the bilingual speakers produced a four-way contrast (statistically significant: p<0.05): long lead VOT (CF /b, d/), short lead VOT (CE /b, d/), short-lag VOT (CF /p, t/) and long-lag VOT (CE /p,t/). These results suggest that bilinguals are maintaining phonetic contrasts both within and across their two languages.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".