Mothers of Invention: How the Experiences of Women Working From Home During COVID-19 Could Reshape the Domestic Environment
Bibliographic record
Abstract
Despite the desire of the postindustrial workforce, particularly women, for flexible work arrangements, only 4% of Canadian employees performed their job duties remotely before the pandemic. However, this segment grew dramatically in March 2020 when the COVID-19 lockdowns forced office employees to work from home (WFH). Because the merging of employment with the dwelling has affected the genders unevenly, we focused on the WFH experiences of women, living in the metropolitan area of Vancouver, British Columbia, Canada, with occupations that could be performed remotely during the pandemic. We further explored how women used their agency to overcome material and behavioral challenges encountered in the home workspace by implementing innovative modifications. Using a mixed-methods approach, data were collected from 96 women with an online questionnaire, followed by 15 semi-structured interviews. The results showed that each participant created a functional workspace (if one did not already exist), and successfully performed their paid employment at home. Despite the difficulties—some resulting from the pandemic—that complicated WFH, almost every woman wanted to continue working remotely in some capacity. The findings suggest that remote work is a viable labor model for women who want to combine paid and unpaid labor in a WFH nexus within the dwelling. Examples of home-workspace innovations are provided, revealing new design considerations that could influence residential design—especially in smaller homes—as the post-pandemic labor force evolves to include a larger segment of remote employees.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".