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Record W2912227975 · doi:10.3138/jcfs.48.4.383

Trends in the Divorce Rate and its Regional Disparity in China

2017· article· en· W2912227975 on OpenAlexvenueno aff
Li Mo

Bibliographic record

VenueJournal of Comparative Family Studies · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicDemographic Trends and Gender Preferences
Canadian institutionsnot available
Fundersnot available
KeywordsChinaBeijingDemographic economicsGeographyDevelopment economicsDistribution (mathematics)DemographyEconomic geographyPolitical scienceSocioeconomicsEconomic growthEconomicsSociology

Abstract

fetched live from OpenAlex

With rapid economic growth, China has undergone substantial social, cultural and ideological transformations over the recent decades. In the meantime, trends in China’s family structure have changed dramatically as well. However, due to data limitations, research on trends in divorce has been very rare in China; especially the quantitative studies at the macrolevel. The literature indicates that despite the very low divorce rate from the 1960s to the 1970s, China’s divorce rate has increased greatly in recent decades, but this increase has been uneven in both space and time. Therefore, this paper analyzes trends in China’s divorce rate at the national, regional and provincial levels. The research results suggest that China’s divorce rate has witnessed a steady and noticeable increase in the recent two decades, with the Crude Divorce Rate (CDR) increasing by 178% and Refined Divorce Rate (RDR) increasing by 211%. Among the four provincial-level municipalities, Chongqing shows strikingly high divorce rates, whereas the divorce rates of Beijing and Shanghai have leveled off in recent years. Among all the provincial level units, the Moslem-majority Xinjiang Uygur Autonomous Region ranks first, whereas Tibet ranks last.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.372
Threshold uncertainty score0.995

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 science0.0000.000
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 head0.270
GPT teacher head0.446
Teacher spread0.176 · 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

Citations15
Published2017
Admission routes1
Has abstractyes

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