Fathers Stepping Up? A Cross-National Comparison of Fathers’ Domestic Labor and Parents’ Satisfaction with the Division of Domestic Labor During the COVID-19 Pandemic (accepted)
Bibliographic record
Abstract
The COVID-19 pandemic disrupted work and family life around the world. For parents, this upending meant a potential re-negotiation of the “status quo” in the gendered division of labor. A comparative lens provides extended understandings of changes in fathers’ domestic work based in socio-cultural context—in assessing the size and consequences of change in domestic labor in relation to the type of work-care regime. Using novel harmonized data from four countries (the United States, Canada, the United Kingdom, and the Netherlands) and a work-care regime framework, this study examines cross-national changes in fathers’ shares of domestic labor during the early months of the pandemic and whether these changes are associated with parents’ satisfaction with the division of labor. Results indicate that fathers’ shares of housework and childcare increased early in the pandemic in all countries, with fathers’ increased shares of housework being particularly pronounced in the US. Results also show an association between fathers’ increased shares of domestic labor and mothers’ increased satisfaction with the division of domestic labor in the US, Canada, and the UK. Such comparative work promises to be generative for understanding the pandemic’s imprint on gender relations far into the future.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
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".