What do bilingual infants actually hear? Evaluating measures of language input to bilingual-learning 10-month-olds
Bibliographic record
Abstract
Examining how bilingual infants experience their dual language input is important for understanding bilingual language acquisition. To assess these language experiences, researchers typically conduct language interviews with caregivers. However, little is known about the reliability of these parent reports in describing how bilingual children actually experience dual language input. Here, we explored the quantitative nature of dual language input to bilingual infants. Further, we described some of the heterogeneity of bilingual exposure in a sample of French-English bilingual families. Participants were twenty-one families with a 10-month-old infant residing in Montréal, Canada. First, we conducted language interviews with the caregivers. Then, each family completed three full-day recordings at home using the LENA (Language Environment Analysis) recording system. Results showed that children’s proportion exposure to each language was consistent across the two measurement approaches, indicating that parent reports are reliable for assessing a bilingual child’s language experiences. Further exploratory analyses revealed three unique findings: (1) there can be considerable variability in the absolute amount of input among infants hearing the same proportion of input, (2) infants can hear different proportions of language input when considering infant-directed versus overheard speech, (3) proportion of language input can vary by day, depending on who is caring for the infant. We conclude that collecting naturalistic recordings is complementary to parent-report measures for assessing infant’s language experiences and for establishing bilingual profiles.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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; both teacher heads agree on what is shown here.
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".