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Record W4210487955 · doi:10.3138/jcfs.52.4.03

Distribution of Household Labor Between Mothers and Fathers in Rural and Urban Malaysia

2022· article· en· W4210487955 on OpenAlexvenueno aff
Ziarat Hossain, Zainal Madon

Bibliographic record

VenueJournal of Comparative Family Studies · 2022
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
Fundersnot available
KeywordsMalayUrbanizationContext (archaeology)ResidenceSocioeconomicsLaundryDemographic economicsGeographyPsychologySociologyEconomic growthDemographyEconomics

Abstract

fetched live from OpenAlex

This study examined whether the distribution of household labor including childcare in Malay families varied as a function of the gender of parents and their rural-urban residence. Using a convenience-sampling approach, we interviewed mothers and fathers from 50 rural and 50 urban intact Malay families in peninsular Malaysia. We employed the tenets of the bioecological systems theory to interpret the findings. Multivariate analysis of variance revealed that mothers spent more time doing housework, laundry, childcare, and preparing meals than fathers did and fathers spent more time in maintenance and shopping for food than mothers did in both rural and urban families. Whereas urban fathers spent more time in childcare and shopping for food than their rural counterparts did, mothers and fathers in urban families equally participated in keeping track of expenses. The discrepancy between mothers’ and fathers’ time engagement in childcare was less in urban families than it was in rural families. Mothers were more engaged in traditional areas of household labor than fathers and compared to rural fathers, urban fathers spent more time in most household tasks including childcare. In view of rapid urbanization and multiethnic social context, the current findings are important because they highlight the contemporary patterns of parental engagement in household labor including childcare in understudied Malay families.

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)0.001
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 score0.226
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.079
GPT teacher head0.344
Teacher spread0.265 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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