MétaCan
Menu
Back to cohort
Record W4399623035 · doi:10.1080/17441730.2024.2362010

Housework sharing among older couples: explaining the gendered division of domestic labour in older age in South Korea

2024· article· en· W4399623035 on OpenAlexafffund
Seung-Eun Cha, Jooyeoun Suh, Kamila Kolpashnikova

Bibliographic record

VenueAsian Population Studies · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of CanadaUniversity of Suwon
KeywordsWifeDivision of labourDemographic economicsDistribution (mathematics)IdeologyDual (grammatical number)SociologyPsychologyGender studiesLabour economicsEconomicsPolitical science

Abstract

fetched live from OpenAlex

Our study investigates the relationship between family models and housework division among older couples. Using the 2019 Korean Time Use Survey, we analysed wives’ share of housework in four family models—dual-earner, traditional (husband-breadwinner), wife-breadwinner, and retired (non-employed) couples—in which at least one partner was aged 65 or above (N = 1,564). Results show that although wives’ housework share varies across the four family models, unequal distribution of housework persists in older age, with wives shouldering over 70 per cent of the total housework regardless of the family model. Wives’ housework share in wife-breadwinner couples was significantly lower than that among dual earners. We also found that economic resources, particularly income, and gender ideology play a limited role in explaining the division of housework among older couples. However, health played a crucial role, with wives and husbands doing more housework when their partners reported poor health.

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)0.001
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 score0.013
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
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 head0.058
GPT teacher head0.354
Teacher spread0.296 · 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

Citations8
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueAsian Population StudiesSame topicWork-Family Balance ChallengesFrench-language works237,207