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Record W3012053479 · doi:10.1007/s13524-019-00849-4

Parental Migration and Early Childhood Development in Rural China

2020· article· en· W3012053479 on OpenAlexaboutno aff
Ai Yue, Yu Bai, Yaojiang Shi, Renfu Luo, Scott Rozelle, Alexis Medina, Sean Sylvia

Bibliographic record

VenueDemography · 2020
Typearticle
Languageen
FieldNursing
TopicChild Nutrition and Water Access
Canadian institutionsnot available
FundersHigher Education Discipline Innovation ProjectUBS Optimus FoundationNational Natural Science Foundation of ChinaChina Medical Board
KeywordsCognitive developmentChinaChild developmentQuarter (Canadian coin)CognitionDemographyDevelopmental psychologyEarly childhoodPsychologyRural areaGerontologyGeographyMedicineSociology

Abstract

fetched live from OpenAlex

Nearly one-quarter of all children under age 2 in China are left behind in the countryside as parents migrate to urban areas for work. We use a four-wave longitudinal survey following young children from 6 to 30 months of age to provide first evidence on the effects of parental migration on development, health, and nutritional outcomes in the critical first stages of life. We find that maternal migration has a negative effect on cognitive development: migration before children reach 12 months of age reduces cognitive development by 0.3 standard deviations at age 2. Possible mechanisms include reduced dietary diversity and engagement in stimulating activities, both known to be causally associated with skill development in early life. We find no effects on other dimensions of physical and social-emotional health.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.113
Threshold uncertainty score0.225

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.008
GPT teacher head0.216
Teacher spread0.208 · 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

Citations88
Published2020
Admission routes1
Has abstractyes

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