Linking Quality and Quantity of Parental Linguistic Input to Child Language Skills: A Meta-Analysis
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Bibliographic record
Abstract
This meta-analysis examined associations between the quantity and quality of parental linguistic input and children's language. Pooled effect size for quality (i.e., vocabulary diversity and syntactic complexity; k = 35; N = 1,958; r = .33) was more robust than for quantity (i.e., number of words/tokens/utterances; k = 33; N = 1,411; r = .20) of linguistic input. For quality and quantity of parental linguistic input, effect sizes were stronger when input was observed in naturalistic contexts compared to free play tasks. For quality of parental linguistic input, effect sizes also increased as child age and observation length increased. Effect sizes were not moderated by socioeconomic status or child gender. Findings highlight parental linguistic input as a key environmental factor in children's language skills.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 it