The gendered challenges of working from home in 2020-2021: an Australian-Canadian study
Bibliographic record
Abstract
With the declaration of the COVID-19 pandemic in 2020, academics in many countries moved rapidly to a model of working from home, and teleworking became the enforced mode of work. For some staff this was a positive experience with less travel time, while others struggled with new technology resulting in increased workloads and stress. There is extensive international evidence that these impacts were gendered, with more women, especially women with care-giving responsibilities, suffering detrimental outcomes. This chapter examines the challenges and experiences of working from home during the pandemic using data from surveys of over 5,000 Canadian and Australian academics. It focuses on two sets of questions — one asking whether academics’ experiences were positive or negative, and why, and one asking about the proportion of time people wish to be working at home — and investigates the reasons that drive positive or negative responses to working from home.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.008 |
| Science and technology studies | 0.033 | 0.008 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".