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Record W2975180584 · doi:10.3138/jcfs.50.2.003

Chinese Parents’ Effect on Children’s Math and Science Achievements in Schools with Different SES

2019· article· en· W2975180584 on OpenAlexvenueno aff
Guiqing An, Jingying Wang, Yang Yang

Bibliographic record

VenueJournal of Comparative Family Studies · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicParental Involvement in Education
Canadian institutionsnot available
Fundersnot available
KeywordsContext (archaeology)Socioeconomic statusDevelopmental psychologyPsychologyMathematics educationDemographyGeographySociologyPopulation

Abstract

fetched live from OpenAlex

Chinese educational system is comprised of school, family and community, and coordination between school and family is the context for school management. Family education is the foundation of personal development with advantage in education time, content and techniques. This study chooses different school districts with different socio-cultural and economic backgrounds to compare and study the ways of parental involvement in different school districts and its impact on children’s STEM achievements in mathematics and science. In addition, the study also discussed the influence of parental involvement on children’s mathematics, physics, biology and geography in different family social economic status (SES) school districts. Generally speaking, parents’ expectations ranked first among the four areas, followed by parents’ communication, while school participation lies in the lowest. The lowest SES districts use the most parental control methods, and the highest SES area uses the most parental communication methods. The top two areas of SES use the most school communication methods. Furthermore, compared with girls, parents’ school participation has a significant negative impact on boys in the highest and lowest areas. Family communication has more influence on girls than boys. In school communication, it has the least impact on boys and girls.

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 score0.005
Threshold uncertainty score0.420

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 communication0.0000.001
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 head0.055
GPT teacher head0.402
Teacher spread0.347 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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