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Record W2913339801 · doi:10.3138/jcfs.48.2.261

Division of Household Labor and Marital Satisfaction in China: Urban and Rural Comparisons

2017· article· en· W2913339801 on OpenAlexvenueno aff
Bryan C. Kubricht, Richard B. Miller, Kang-lin Yang, James M. Harper, Jonathan G. Sandberg

Bibliographic record

VenueJournal of Comparative Family Studies · 2017
Typearticle
Languageen
FieldPsychology
TopicAttachment and Relationship Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsChinaDivision of labourDivision (mathematics)Marital statusDemographic economicsSocioeconomicsRural areaGeographyEconomicsDemographyPopulationSociologyPolitical science

Abstract

fetched live from OpenAlex

Division of household labor is associated with marital satisfaction among Chinese populations. However, little research has compared rural and urban regions in Chinese societies, which have traditionally had significant cultural differences. This study compared the level of division of household labor and the relationship of division of household labor and marital satisfaction between the urban and rural regions of China. ANOVA results indicated that there were no urban-rural differences in the division of household labor among males and among females. Overall, the division of household labor was significantly associated with marital satisfaction, and invariance testing using SEM revealed the strength of the relationship between division of labor and marital satisfaction did not differ among urban and rural regions in China. Females reported higher levels of household labor in both rural and urban regions, but there were no gender differences in the relationship between division of household labor and marital satisfaction.

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)0.000
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 score0.011
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.092
GPT teacher head0.439
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

Citations10
Published2017
Admission routes1
Has abstractyes

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