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Record W4200488294 · doi:10.3138/jcfs-2021-0065

The Changing Patterns and Determinants of Stay-at-Home Motherhood in Urban China, 1982 to 2015

2021· article· en· W4200488294 on OpenAlexvenueno aff
Zheng Mu, Felicia F. Tian

Bibliographic record

VenueJournal of Comparative Family Studies · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsChinaCensusOvertimeDemographic economicsDivision of labourEconomic growthPopulationSociologyGeographyPolitical scienceLabour economicsEconomicsDemography

Abstract

fetched live from OpenAlex

This paper documents trends in and examines determinants of stay-at-home motherhood in urban China from 1982 to 2015. China once had the world’s leading female labor force participation rate. Since the economic reforms starting from the early 1980s, however, some mothers have been withdrawing from the labor force due to diminished state support, a rise in intensive parenting, and heightened work-family conflicts. Based on data from the 1982, 1990, and 2000 Chinese censuses, the 2005 mini-census, and the 2006–2015 Chinese General Social Survey, we find mothers’ non-employment increased for every educational group and grew at a much faster rate among mothers than it did among fathers, particularly those with small children. Moreover, the negative relationships between mothers’ education and non-employment, and between mothers’ family income and non-employment weakened overtime. This is possibly due to women with more established resources can better “afford” the single-earner arrangement and also more emphasize the importance of intensive parenting, than their less resourced counterparts. These findings signal the resurgence of a gendered division of labor in urban China.

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 score0.024
Threshold uncertainty score0.601

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.000

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 head0.071
GPT teacher head0.381
Teacher spread0.310 · 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

Citations17
Published2021
Admission routes1
Has abstractyes

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