Bibliographic record
Abstract
We investigate Voice Onset Time (VOT) of voiceless stops in conversational speech in a transitional bilingual context. We examine the speech of three generations of bilinguals whose Heritage Language (HL) is one of three European languages (Italian, Russian, or Ukrainian) and who also speak English. The data are extracted from recordings of sociolinguistic interviews conducted in Toronto that are contained in the Heritage Language Documentation Corpus (Nagy 2009). We examine word-initial /p, t, k/ in stressed syllables before /a/ and /o/ (~150 tokens per speaker), produced by 18 individuals representing three to five generations of speakers in each language. Unlike in English, voiceless stops in Italian, Russian, and Ukrainian are realized with a short lag VOT, defined as < 30 ms. Comparison of the HL patterns to previously published results on the VOT of monolingual speakers of these languages and monolingual speakers of English illustrates contact-induced influence: across the generations, the VOT of these speakers drifts away from the monolingual short lag toward the long lag of English for Russian and Ukrainian. Puzzlingly, the cross-generational change is (slightly) in the opposite direction for Italian. We discuss possible reasons for these different outcomes as well as contrasting them with the lack of cross-generational change found in analyses of pro-drop in the same corpus.
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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".