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Record W2148701917 · doi:10.1177/019251300021003002

Economic Circumstances and the Stability of Nonmarital Cohabitation

2000· article· en· W2148701917 on OpenAlexaff
Zheng Wu, Michael Pollard

Bibliographic record

VenueJournal of Family Issues · 2000
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsCohabitationEarningsPanel Study of Income DynamicsEconomicsDemographic economicsSeparation (statistics)Independence (probability theory)Labour economicsEconomic independenceDevelopment economicsPolitical science

Abstract

fetched live from OpenAlex

Using data from the first two waves of Survey of Labour and Income Dynamics (1993 to 1994), this article examines the role of economic circumstances in cohabitation dissolution through legal marriage or union separation. Data are analyzed using discrete-time event history methods separately for women and men and for each of the competing exits. The results show economic deprivation contributes to union instability; an overall deterioration in household economic circumstances increases the risk of union separation. Conversely, for both cohabiting women and men, an increase in personal earnings raises the likelihood of union separation, indicative of an independence effect. Whereas semiprofessional and skilled women are more likely to dissolve their unions through separation, professional and semi-professional men are more apt to marry their partners. Furthermore, women with incomes below the low-income cut-off point are more likely to separate than women with higher incomes. Implications of these results are discussed.

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)0.008
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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
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)0.0020.000

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 head0.026
GPT teacher head0.307
Teacher spread0.280 · 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

Citations69
Published2000
Admission routes1
Has abstractyes

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