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Record W2602479074 · doi:10.1177/0164027517698024

Marital and Cohabiting Union Dissolution in Middle and Later Life

2017· article· en· W2602479074 on OpenAlexaffabout
Zheng Wu, Margaret J. Penning

Bibliographic record

VenueResearch on Aging · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCohabitationRemarriageDemographyMarital statusHazardHazard ratioDemographic economicsPsychologyPolitical scienceMedicineSociologyEconomicsPopulationConfidence interval

Abstract

fetched live from OpenAlex

This study examined the timing and risk factors for subsequent union disruption among individuals who were in a marital or cohabiting union at age 45, focusing particularly on the role of prior union history and children. Using retrospective data on union histories from the 2007 Canadian General Social Survey ( n = 17,194), the results of life-table analysis revealed that individuals in cohabiting relationships faced a greater risk of union disruption in middle or later life than those who were married. Cox proportional hazard models showed that both union biography (duration, remarriage/repartnership) and family biography (children born inside/outside union, child age) influenced union dissolution through separation or divorce, but their impact differed depending on union type and gender. These findings suggest that when it comes to marriage and cohabitation, the middle and later years of life are neither a clear continuation nor a complete departure from earlier patterns.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.059
Threshold uncertainty score0.118

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.231
GPT teacher head0.451
Teacher spread0.220 · 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 source (direct Gemma or distilled Codex), 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

Citations22
Published2017
Admission routes2
Has abstractyes

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