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Record W4299724422 · doi:10.31235/osf.io/ksvgh

Women’s Fertility Autonomy in Urban China: The Role of Couple Dynamics Under the Universal Two-Child Policy

2017· preprint· en· W4299724422 on OpenAlexaff
Yue Qian, Yongai Jin

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsFertilityAutonomyChinaPower (physics)Bargaining powerNegotiationDemographic economicsEconomicsPsychologyPolitical scienceSociologyDemographyPopulationSocial science

Abstract

fetched live from OpenAlex

Under China’s universal two-child policy, decisions about whether to have a second birth become more dynamic, flexible, and subject to negotiation between the spouses; moreover, how women can maintain their fertility autonomy has far-reaching implications for gender equality. Using valuable, new data from the 2016 Survey of the Fertility Decision-Making Processes in Chinese Families, we examine the relationship between couple dynamics and women’s fertility autonomy in urban China. If women want no more than one child and already have one, intending to have a second birth indicates low fertility autonomy. Couple dynamics are measured by conjugal power structure and spousal pressure on fertility. We find that only if women have less marital power than their husbands, greater fertility pressure from husband is associated with a higher likelihood that women intend to have a second birth. In addition, when investigating the determinants of couple dynamics, we find that women’s marital power depends on their relative resources, whereas fertility pressure from husband persists regardless. The findings suggest that in post-reform urban China, growing gender inequalities in labor markets likely reduce women’s marital power, which in turn negatively affects their fertility autonomy. We urge greater research and policy attention to gender equality issues in the era of the universal two-child policy.

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)0.002
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.519
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0000.001
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 head0.020
GPT teacher head0.292
Teacher spread0.273 · 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 designTheoretical or conceptual
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

Citations4
Published2017
Admission routes1
Has abstractyes

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