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Gendered perceptions of workaholism and the gender gap in parenting time

2020· article· en· W3096814488 on OpenAlexaffabout
Tom Buchanan, Adian McFarlane, Anupam Das

Bibliographic record

VenueFamilies Relationships and Societies · 2020
Typearticle
Languageen
FieldPsychology
TopicWorkaholism, burnout, and well-being
Canadian institutionsWestern UniversityMount Royal University
Fundersnot available
KeywordsPsychologyPerceptionWork hoursWork (physics)Domestic workGender gapTime-use surveyDevelopmental psychologyDemographic economicsWorking hoursLabour economicsEconomics

Abstract

fetched live from OpenAlex

We use the 2015 Canadian time-use survey to analyse predictors of workaholism and the gender gap in parenting time. For mothers, both parenting time and market work are predictive of self-reporting workaholism. Workaholic mothers do not spend less time parenting as their market work increases. These levels remain higher at each level than workaholic fathers and non-workaholic mothers and fathers. This suggests increased market work time does not result in any reduction of relative levels of parenting time. Along with entrenched gendered expectations, mothers may perceive an opportunity cost of sacrificing parenting time for market work. Workaholic mothers do report reduced household labour hours. Accounting for characteristic differences between workaholic mothers and fathers leaves more unexplained than explained. Implications are discussed within the framework of gender and opportunity costs.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.573
Threshold uncertainty score0.858

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.057
GPT teacher head0.280
Teacher spread0.223 · 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

Citations4
Published2020
Admission routes2
Has abstractyes

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