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Enhancing Language Acquisition in Children from Lower Socioeconomic Backgrounds: The Role of Parental Involvement

2024· article· en· W4403844785 on OpenAlexaff
H. J. Yang

Bibliographic record

VenueLecture Notes in Education Psychology and Public Media · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSocioeconomic statusDevelopmental psychologyPsychologyLanguage acquisitionEnvironmental healthMedicineMathematics educationPopulation

Abstract

fetched live from OpenAlex

Early language acquisition is fundamental to a child’s future academic success. The development of vocabulary and oral storytelling skills at a young age serves as a strong foundation for later achievements in overall literacy. Disparities in language abilities become apparent by kindergarten and tend to persist throughout a child’s educational journey. Therefore, prioritizing early language development is crucial to ensuring that all children could reach their full potential. This literature review examines three effective interventions - the 3Ts Home Visiting Curriculum, Too Small to Fail, and the STELLA curriculum - designed to improve language acquisition and development in children from lower socioeconomic status (SES) backgrounds. It provides brief descriptions of each intervention, analyzes the underlying psychological principles of language acquisition and development, and considers the implications of these global approaches. Parental involvement is crucial in creating and sustaining the success of these programs. Future directions to enhance the effectiveness, accessibility, and inclusivity of language development interventions for targeted children are explored.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.332
Teacher spread0.318 · 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.

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
Published2024
Admission routes1
Has abstractyes

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