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Record W4412734017 · doi:10.37306/xa0r6w06

Family Education Strategies in Supporting Early Childhood Development

2025· article· en· W4412734017 on OpenAlexaboutno aff
Imam Muthie, Khairul Alfahani, Ary Kiswanto Kenedi, Al Wafi

Bibliographic record

VenueJurnal Keluarga Berencana · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsnot available
Fundersnot available
KeywordsEarly childhood educationEarly childhoodDevelopmental psychologyPsychology

Abstract

fetched live from OpenAlex

High-quality research on family education has increased significantly over the past decade, with 1,040 documents recorded in Scopus. This study aims to analyze research trends, identify key terms, highlight the most active journals, examine author collaboration and citation networks, determine author affiliations and funding sources, map contributions by country, and explore related scientific fields. Data were collected from Scopus.com and analyzed using VOSViewer. The results show a positive trend with steady annual growth. Family education is closely linked to early childhood education, parenting, early intervention, and overall quality of life. Frontiers in Psychology is the most active journal in its field. Landier, W., is the most influential author. The top contributing institutions include Harvard Medical School and the University of Washington. Primary funding sources are the National Institutes of Health and the U.S. Department of Health and Human Services. The United States leads in publication output, followed by China and Canada. Most articles are from the fields of medicine and social sciences. These findings highlight family education as a growing multidisciplinary field with wide-ranging impacts. The results guide policymakers in creating targeted parental training on digital literacy and emotional regulation, while education practitioners can apply family-centered interventions to enhance parental involvement in early learning.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.376
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.027
GPT teacher head0.353
Teacher spread0.326 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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