The Influence of Heritage Language Experience on Perception and Imitation of Prevoicing
Bibliographic record
Abstract
This work tests the effect of heritage language background on imitation and discrimination of prevoicing in word-initial stops. English speakers with heritage languages of Spanish (where prevoicing is obligatorily present) or Cantonese (where prevoicing is obligatorily absent), as well as monolingual English speakers, imitated and discriminated pairs of stimuli differing minimally in prevoicing, both in English (participants’ dominant language) and Hindi (a foreign language), and they also completed a baseline word reading task. Heritage speakers of Spanish were expected to show the highest performance on both imitation and discrimination, given the contrastive status of prevoicing in Spanish. Spanish speakers did indeed show more faithful imitation, but only for Hindi, not English, sounds, suggesting that imitation performance can differ based on language mode. On the other hand, there were no group differences in imitation of prevoicing in English or in discrimination in either language. Imitation was well above chance in all groups, with substantial within-group variability. This variability was predicted by individual discrimination accuracy, and, for Cantonese speakers only, greater prevoicing in baseline productions corresponded with more faithful imitation. Overall, despite an expectation for differences, given previous evidence for the influence of heritage languages on production and perception of English voiced stops, our results point to a lack of cross-language influence on perception and imitation of English prevoicing.
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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 | 0.004 |
| 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) | 0.003 | 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".