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Record W3128683352 · doi:10.1111/cdev.13508

Linking Quality and Quantity of Parental Linguistic Input to Child Language Skills: A Meta-Analysis

2021· review· en· W3128683352 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueChild Development · 2021
Typereview
Languageen
FieldPsychology
TopicLanguage Development and Disorders
Canadian institutionsUniversity of TorontoYork UniversityAlberta Children's HospitalUniversity of Calgary
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPsychologyLinguisticsMeta-analysisQuality (philosophy)Developmental psychologyLanguage developmentLinguistic analysisLinguistic performance

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.878
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.002
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.102
GPT teacher head0.421
Teacher spread0.319 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it