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Record W2778149455 · doi:10.1080/13545701.2017.1407032

Childcare, Household Composition, Muslim Ethnicity, and Off-Farm Work in Rural China

2017· article· en· W2778149455 on OpenAlexaff
Ding Sai, Xiao‐yuan Dong, Margaret Maurer‐Fazio

Bibliographic record

VenueFeminist Economics · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicMigration and Labor Dynamics
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsEthnic groupGrandparentChinaDemographic economicsSurvey data collectionIntersectionalitySociologyGender studiesDemographyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

This study explores how religious and ethnic norms and gender relations interact across the domestic and public spheres of work in rural China's minority-concentrated regions. It focuses on the roles that childcare and household composition play in the employment decisions of prime-age married individuals of Muslim and non-Muslim ethnicity. Using the 2012 China Household Ethnicity Survey (CHES), the study finds that children generally decrease women's likelihood of employment outside the home and increase men's. The gender gap in the probability of off-farm employment is larger for those of Muslim ethnicity. Non-Muslim parents of sons are more likely to migrate for employment than parents of daughters. The presence of women of grandparent age (46–70) universally facilitates labor migration. Men of grandparent age tend to increase only the probability that non-Muslim parents migrate for employment. Additional adult male household members reduce the likelihood that women of Muslim ethnicity have off-farm employment.

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 score0.065
Threshold uncertainty score0.128

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 studies0.0010.001
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 head0.021
GPT teacher head0.276
Teacher spread0.255 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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