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Record W2789476126 · doi:10.1111/jomf.12470

Educational Assortative Mating and Income Dynamics in Couples: A Longitudinal Dyadic Perspective

2018· article· en· W2789476126 on OpenAlexafffund
Yue Qian

Bibliographic record

VenueJournal of Marriage and the Family · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicFamily Dynamics and Relationships
Canadian institutionsUniversity of British Columbia
FundersUniversity of British Columbia
KeywordsAssortative matingLife course approachDyadPsychologyDemographic economicsPerspective (graphical)Educational attainmentLongitudinal dataInequalityLongitudinal studyEconomicsSocial psychologySociologyDemographyPopulationEconomic growth

Abstract

fetched live from OpenAlex

The question of how educational assortative mating may transform couples' lives and within‐family gender inequality has gained increasing attention. Using 25 waves (1979–2012) of data from the National Longitudinal Survey of Youth 1979 and longitudinal multilevel dyad models, this study investigated how educational assortative mating shapes income dynamics in couples during the marital life course. Couples were grouped into three categories—educational hypergamy (wives less educated than their husbands), homogamy, and hypogamy (wives more educated than their husbands). Results show that change in husbands' income with marital duration is similar across couples, whereas change in wives' income varies by educational assortative mating, with wives in educational hypogamy exhibiting more positive change in income during the marital life course. The finding that husbands' long‐term economic advancement is less affected than that of wives by educational assortative mating underscores the gender‐asymmetric nature of spousal influence in heterosexual marriages.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.543
Threshold uncertainty score0.340

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.017
GPT teacher head0.316
Teacher spread0.299 · 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

Citations23
Published2018
Admission routes2
Has abstractyes

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