Cross-language Influence in the Stop Voicing Contrast in Heritage Tagalog
Bibliographic record
Abstract
In heritage bilinguals’ sound structure, some aspects of the sound system are more prone to cross-language influence than others. In this study, we compare two different models of crosslanguage influence, a phonological markedness based model, which proposes that influence selectively affects a phonologically marked structure, and a phonetic category based model, where influence is mediated through cross-language equivalence classification of similar phones. The empirical data for the study comes from the production of the voicing contrast in English and Tagalog stops by heritage Tagalog speakers in Toronto. We compare the heritage speakers’ production with native control productions and also probe the effect of lexical stress in voicing realization as evidence for the underlying target structure of stop categories. The key empirical findings are that the heritage speakers produce their voiceless stops in both languages nearly native-like, including a native-like stress effect, but voiced stops exhibit considerable crosslanguage influence and assimilatory stress effects. We propose that the heritage speakers successfully establish separate phonetic categories for English and Tagalog voiceless stops, but form a partially merged category for English and Tagalog voiced stops. The findings provide partial support for the phonetic category based model of influence over the phonological markedness based model.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".