Cross-generational Phonetic Drift in Toronto Heritage Tagalog: a Variationist Sociolinguistic Investigation of VOT in Spontaneous Speech
Bibliographic record
Abstract
Abstract This study provides a variationist sociolinguistic investigation of voice onset time ( VOT ) of voiceless stops in Tagalog, focusing on two generations of heritage speakers in Toronto, with the goal of documenting patterns of variation and illuminating the linguistic and social mechanisms that underlie variability. Data on Tagalog drawn from naturalistic speech were examined, and comparisons between heritage speakers and age-matched homeland speakers in Manila reveal that while VOT among homeland speakers shows no signs of change, VOT among heritage speakers demonstrates cross-generational drift towards the long-lag VOT of English, which may be accounted for by cross-linguistic influence. VOT variation is further modulated by gender and ethnic identity, suggesting that at least some variation may be explained by sociolinguistic motivations. Thus, VOT patterns among heritage speakers are a product of a complex interaction of language-internal and social factors.
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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 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".