Dependence of corresponding phonetic categories in native bilingual speakers and in monolingual overhearers of English and French
Bibliographic record
Abstract
Our research addresses two main questions. First, among phonetic categories that correspond, but are not identical, in the two languages of native bilingual speakers, are there cross-language influences on speech production? Specifically, are the voiceless voice-onset times (VOTs) of bilingual speakers of English and French in Montreal longer in their French speech and shorter in their English speech than the VOTs of monolingual speakers of the two languages in Montreal? Our findings suggest a positive answer. Second, does mere overhearing of a language that one does not speak have an impact on production of phonetic categories? To answer this, we examined the voiceless VOTs of monolingual speakers of French and English in Montreal (who hear substantial unaccented English and French, respectively) with those, respectively, of monolingual speakers of French in Paris and of English in Connecticut. Our answer here was no. I discuss the implications of these findings for an understanding of the phonetic knowledge systems of monolingual and natively bilingual speakers.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".