Who’s doing more and when? Gender, parenting, and housework trajectories.
Bibliographic record
Abstract
= 520, 51% female, 39% with a university degree, 90% White), this study examined trajectories of women's and men's contributions to cooking, kitchen cleaning, grocery shopping, house cleaning, laundry, and overall housework from Age 25 to 50 years and explored time-invariant (traditional gender role attitudes, homemaker mother, mother and father education assessed at Age 18) and time-varying (raising children at Ages 25, 32, 43, and 50 years) predictors of housework trajectories. Growth curve analyses revealed that women contributed more to all housework tasks than men at Age 25, a gender gap maintained to Age 50. Housework increased to Age 32 and stabilized until Age 43 before declining by Age 50 for women's and men's laundry, women's kitchen cleaning, grocery shopping, and overall housework, and men's house cleaning. There was no change in women's and men's trajectory of cooking meals, women's house cleaning, and men's contributions to kitchen cleaning, grocery shopping, and overall housework. Traditional gender role attitudes, having a homemaker mother, and mother's and father's education inconsistently predicted women's and men's trajectories. Raising children, however, was consistently linked with within-person fluctuations in housework. When raising children, women contributed more than average to housework, whereas when men were raising children, they contributed less than normal. The results highlight a gendered pattern of housework evident in the twenties and persisting well into midlife, with parenthood widening the gap. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".