Language preference in monolingual and bilingual infants.
Bibliographic record
Abstract
Previous research shows that infants in single-language families have some basic language discrimination abilities at birth which improve rapidly over the first 6 months of life, and that attention to the rhythmic properties of language supports these skills. Babies in monolingual families also prefer listening to their native language over an unfamiliar language when presented samples produced by one bilingual or two monolingual talkers. In this study we investigate the emergence of language-specific speech processing in bilingual infants by comparing language preference patterns in monolingual English, monolingual French, and bilingual English-French infants using a three-way language preference test. Listening times were measured to passages of adult-directed speech from three rhythmically different languages (English, French, Japanese; three talkers per language). Ten-month-olds in the monolingual groups listened equally to all three languages. However, 10-month-old bilinguals showed a significant preference for each native language over Japanese; listening times to English and French were not different. Individual bilingual 10-month-olds preferred the more prevalent native language in their input. These findings indicate bilingual infants listen more selectively when they encounter different languages. The implications of these findings for understanding speech processing in early bilingual acquisition will be discussed. [Work supported by SSHRC.]
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".