Developmental changes and effects of parental interaction on French-English bilingual infants' vocalization rates
Bibliographic record
Abstract
In prior work, we investigated how several factors—social context (Social versus Non-Social & One versus Multiple speakers), speaker context (Mother versus Father), language (English versus French) and language dominance (Dominant versus Non-Dominant) – are related to vocalization rates in 10-month-olds growing up in English/French bilingual families (n = 21). This was accomplished by analyzing naturalistic daylong recordings obtained using the Language Environment Analysis (LENA) system. Here, we examined how these factors are related to vocalization rates in older infants by analyzing LENA recordings obtained when these same infants were 18 months of age (n = 16). Similar to previous findings, preliminary analysis showed a higher proportion of infant vocalizations occurred in social contexts (i.e., presence of adult speech). Moreover, more infant vocalizations occurred in contexts with one speaker as opposed to multiple speakers. In contrast to previous findings, 18-month-olds did not vocalize more when interacting with their mothers compared to their fathers. Infant vocalization rates were comparable in French and English input contexts. However, more infant vocalizations occurred in dominant language than in non-dominant language contexts. These findings will help us understand how parent-infant interactions change over time and shape the vocal behavioral of infants being raised in bilingual families.
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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 | 0.002 |
| 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".