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Record W2975543844 · doi:10.1177/0950017019868623

Hebdomadal Patterns of Compensatory Behaviour: Weekday and Weekend Housework Participation in Canada, 1986–2010

2019· article· en· W2975543844 on OpenAlexaboutno aff
Kamila Kolpashnikova, Man‐Yee Kan

Bibliographic record

VenueWork Employment and Society · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicWork-Family Balance Challenges
Canadian institutionsnot available
FundersH2020 European Research Council
KeywordsDeviance (statistics)Demographic economicsTime-use surveyPsychologySocial psychologyEconomicsEngineering

Abstract

fetched live from OpenAlex

Quantitative housework research focused on aggregate weekly hours, which are inadequate in revealing hebdomadal compensatory behaviour in housework participation because such behaviour is likely to occur on weekends when couples have more time to do housework. This article extends the existing theoretical frameworks by accounting for the hebdomadal patterns in routine and non-routine housework tasks. Employing five time-use waves of the Canadian General Social Survey, our study shows that the hebdomadal compensatory behaviour applies both to women and men. Equal-earner and breadwinner wives compensate for their low levels of weekday housework participation by doing more routine housework on weekends. Similarly, husbands also increase their time on routine housework on weekends. Therefore, compensatory behaviour is more likely to depend on hebdomadal time availability rather than on the neutralisation of gender deviance in the labour market (gender deviance neutralisation). Some evidence of the gender deviance neutralisation, however, cannot be completely ruled out.

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)0.002
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 score0.028
Threshold uncertainty score0.200

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0030.001
Scholarly communication0.0010.000
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.018
GPT teacher head0.265
Teacher spread0.247 · 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

Citations14
Published2019
Admission routes1
Has abstractyes

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