Early Maternal Language Use During Book Sharing in Families From Low-Income Environments
Bibliographic record
Abstract
PURPOSE: The authors examined the language used by mothers from low-income and rural environments with their infants at ages 6 and 15 months to identify predictors of maternal language use at the 15-month time point. METHOD: Maternal language use by 82 mothers with their children was documented during book-sharing interactions within the home in a prospective longitudinal study. The authors analyzed transcripts for maternal language strategies and maternal language productivity. RESULTS: Analyses indicated variability across mothers in their language use and revealed some stability within mothers, as maternal language use at the 6-month time point significantly predicted later maternal language. Mothers who used more language strategies at the 6-month time point were likely to use more of these language strategies at the 15-month time point, even after accounting for maternal education, family income, maternal language productivity, and children's communicative attempts. CONCLUSIONS: Mothers' language use with their children was highly predictive of later maternal language use, as early as age 6 months. Children's communication also influenced concurrent maternal language productivity. Thus, programs to enhance maternal language use would need to begin in infancy, promoting varied and increased maternal language use and also encouraging children's communication.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".