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Record W1913003342 · doi:10.23979/fypr.44958

Education and the division of household labor in dual-earner families

2001· article· en· W1913003342 on OpenAlexaff
Anneli Miettinen

Bibliographic record

VenueFinnish Yearbook of Population Research · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsPopulation Health Research Institute
Fundersnot available
KeywordsDivision of labourDivision (mathematics)Demographic economicsUnpaid workSociologyDual (grammatical number)PoliticsWork (physics)DemographyPsychologyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

This article is the first report of a study on policies and the division of paid and unpaidwork in families in Finland. The article examines the division ofhousehold labor and itsdeterminants in Finnish dual-eamer families. The main objective is to examine whethereducation has any impact on the division ofunpaid work and men's participation in itcontrolling fr other variables. It was found, that among women, rising educationallevels, non-traditional attitudes and younger age cohort had a negative impact on timespent on housework, while among men only reduced time in employment and nontraditionalattitudes increased the contribution at home. While both men and womenwith higher education and non-traditional attitudes were more likely to perceive theirrelative division as more equal, an analysis of the absolute number of hours spent onhousework seems to support the notion that more equal distribution of tasks at home ismore or less a result of younger and educated women doing less housework. The datacomes from a survey conducted in 1998, in which 2,500 Finnish men and women werequestioned about time use, employment, attitudes about gender roles, work and family,andreconciliation ofwork and family. The Finnish study is part ofa Europeanresearchproject which studies the division oflabor in families in different cultural, political andsocietal settings.

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)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.025
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.075
GPT teacher head0.396
Teacher spread0.321 · 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

Citations1
Published2001
Admission routes1
Has abstractyes

Explore more

Same venueFinnish Yearbook of Population ResearchSame topicWork-Family Balance ChallengesFrench-language works237,207