The extreme gendering of COVID−19: Household tasks and division of labour satisfaction during the pandemic
Bibliographic record
Abstract
For many years, scholars have directed our attention to the gender gap in domestic labour. Even when women engage in paid employment, they nevertheless perform the majority of the household labour in most wealthy countries. At the same time, disasters and crises both expose and exacerbate existing social inequalities. In this paper, we ask: in what ways has the COVID-19 pandemic contributed to the gender gap in household labour, including childcare? How do women and men feel about this gap? Using data from the Canadian Perspectives survey series (Wave 3), conducted by Statistics Canada three months into the pandemic, our analyses consider the task distribution that made household labour intensely unequal during COVID-19, with women ten times more likely than men to say childcare fell mostly on them, for example. Yet, in nearly all of our models, women did not ubiquitously report being more dissatisfied with the division of domestic tasks within the house, nor were they more likely than men to say that the household division of labour "got worse" during COVID; however, parents did feel that it got worse. We discuss what these findings mean for women's mental health, long-term paid labour, and interpersonal power, and raise questions about why it is we are not seeing a decrease in women's reported satisfaction with this division of labour. These findings spotlight gender inequality and the family as ongoing pillars of capitalism, and how the structural and interpersonal weathering of the pandemic comes at a particularly great expense to women.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".