Phonetic evidence for early language differentiation: Research issues and some preliminary data
Bibliographic record
Abstract
Although evidence is now available from several domains of language acquisition research that bilingual(BFLA) children differentiate their languages from the time of their earliest productions, studies in the phonetics-phonology domain have been sparse until recently. In this paper we first highlight some methodological issues that impact phonetic-phonological data collection and interpretation. These issues include language context, bilingual versus monolingual mode, and adult listening bias. After suggesting types of acoustic evidence that can be used to determine whether the phonological modules of a young bilingual child are separate, we focus on voice onset time (VOT). We discuss methodological issues specificto a VOT study, including segmental context, rate of speech, and the position of word stress. We also present preliminary data from two BFLA children, ages four and two, learning Japanese and English. Separate recordings were made of the two children as they were asked to identify various pictures. Each child's VOTs were calculated and compared across languages. Results showed that the two-year-old had no significant difference between languages. However, the t -test results for the four-year-old indicated that, for/p/ and /t/, VOT for English was of significantly longer duration than VOT for Japanese.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".