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Record W2116428399 · doi:10.1177/002071520204300204

Determinants of Family Life Satisfaction in Reforming Urban China

2002· article· en· W2116428399 on OpenAlexvenueno aff
Jianjun Ji, Xiaohe Xu, Susan Rich

Bibliographic record

VenueInternational Journal of Comparative Sociology · 2002
Typearticle
Languageen
FieldSocial Sciences
TopicIntergenerational Family Dynamics and Caregiving
Canadian institutionsnot available
Fundersnot available
KeywordsChinaFamily lifeMainland ChinaCohabitationKinshipLife satisfactionPopulationContext (archaeology)Demographic economicsSocioeconomicsEconomic growthGeographySociologyPsychologySocial psychologyDemographyEconomics

Abstract

fetched live from OpenAlex

Since the late 1970s, mainland China has embarked on an unprecedented economic reform. This reform not only helped China move away more successfully from a centrally planned economy than the former Soviet Union and its East European satellites, it also improved the quality of life for China’s massive population, especially its urban population. While the impact of this reform on family life such as household composition, marriage patterns, childbearing decisions, and inter-generational relationships has been well documented, research on family life satisfaction during this political and economic transformation is nearly nonexistent. To fill this research void, we use the 1993 China Housing Survey conducted in Shanghai and Tianjin to explore the determinants of family life satisfaction among married urban Chinese. Our Confirmatory Factor Analysis conceptualizes family life satisfaction as a multi-dimensional concept, encompassing satisfaction with marriage, family economy, family relations, and family life in general. The OLS regression analysis indicates that varying individual characteristics, marital and parental roles, kinship, social and political ties, and community context are predicative of family life satisfaction in urban China. Gender differences in these determinants are noticeable.

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 score0.177
Threshold uncertainty score0.290

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.047
GPT teacher head0.365
Teacher spread0.318 · 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
Published2002
Admission routes1
Has abstractyes

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