Bibliographic record
Abstract
This study examines the gendered division of labour, including both paid and unpaid (housework/childcare) work, during the COVID-19 pandemic.Literature emerging during the pandemic, especially in the first half of 2020, indicated a fear of the pandemic's potential negative impact on women's progress in the labour market, gendered division of labour, and gender equality.In other words, many feared a resurgence of the male-breadwinner family and what it might mean for women.To better understand the division of labour during the COVID-19 pandemic, I use quantitative data from the Labour Force Survey of May 2019, May 2020, May 2021, and May 2022, the Impacts of COVID-19 on Canadians -Parenting during the pandemic, 2020 survey, and the Canadian Perspective Survey Series 3, 2020: Resuming Economic and Social Activities during COVID-19 survey.I also use qualitative data from the subreddits r/Parenting, r/AmItheAsshole, and r/Relationship_advice on Reddit.com.Results indicate that while there was change related to the pandemic in 2020, these changes were temporary.However, this study does find a large gap between the housework and childcare work that women and men do despite comparable employment status.The pandemic does not show permanent negative or positive change to the gendered division of work.However, there is already a pre-existing inequality in housework and childcare between men and women.This inequality is enduring and not easily destabilized in the long run, as showcased by the pandemic.1
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".