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Record W4399295798 · doi:10.1002/rrq.553

Is There a Genetic Confound in the Relation of Home Literacy Environment with Children's Reading Skills? A Familial Control Method Approach

2024· article· en· W4399295798 on OpenAlexaff
Suzhen Zhang, Tomohiro Inoue, George K. Georgiou

Bibliographic record

VenueReading Research Quarterly · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicChild Development and Digital Technology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsReading (process)LiteracyFamily literacyPsychologyRelation (database)Developmental psychologyEmergent literacyPedagogyComputer scienceLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

Abstract We examined the relation between home literacy environment (HLE), parents' reading skills, and children's emergent literacy skills (pinyin letter knowledge, phonological awareness, and vocabulary) and reading (word reading and reading comprehension) in a sample of 168 Chinese children ( M age = 74.26 months) followed from kindergarten to Grade 1. Results of structural equation modeling showed that code‐related HLE activities and access to literacy resources continued to predict children's emergent literacy skills after controlling for the effects of family's socioeconomic status and both parents' reading skills. Parents' reading skills also exerted a direct effect on children's reading comprehension. These findings suggest that HLE exerts a true environmental effect on children's reading skills that is not due to a genetic confound.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.006
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.037
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0030.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.351
Teacher spread0.327 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations9
Published2024
Admission routes1
Has abstractyes

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