“I Was Facilitating Everybody Else’s Life. And Mine Had Just Ground to a Halt”: The COVID-19 Pandemic and its Impact on Women in the United Kingdom
Bibliographic record
Abstract
A growing body of research has highlighted the disproportionately negative impact of the COVID-19 pandemic on women globally. This article contributes to this work by interrogating the lived realities of sixty-four women in the United Kingdom through semi-structured in-depth interviews, undertaken during the first and second periods of lockdown associated with COVID-19 in 2020. Categorizing the data by subgroup of women and then by theme, this article explores the normative and policy-imposed constraints experienced by women in 2020 with regard to paid and unpaid labor, mental health, access to healthcare services, and government representation and consideration of women. These findings highlight women's varied and gendered experiences of the COVID-19 pandemic and emphasizes the role that government can proactively play in attending to gender inequalities throughout its COVID-19 response.
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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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.013 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".