Women Through the COVID-19 Pandemic: Challenges, Consequences, and Resilience
Bibliographic record
Abstract
The COVID-19 pandemic represents an unprecedented event in contemporary history, with far-reaching repercussions for the global economy and society. This article examines the economic challenges and consequences of this pandemic for women. It further explores the pandemic effects on women’s health and well-being, exacerbated by the limited access to basic healthcare and mental health resources, and it points out the challenges facing women in frontline occupations (namely, healthcare). This article also highlights the alarming surge in domestic violence and abuse against women during the pandemic, aggravated by lockdown measures and isolation from support networks. In addition, this article discusses various social and political implications of this pandemic for women, and it reveals how women demonstrated significant resilience over the pandemic-related struggles. The implications of the COVID-19 pandemic are likely to persist in the post-pandemic era as they intersect with ongoing social and economic transformations and new events/crises. At this point, it remains to be determined to what extent this pandemic has decelerated (or even reversed) the progress that was made over the past few decades in terms of reducing gender inequality and enhancing women’s social status, and to what degree women’s resilience in the face of this pandemic has mitigated its adverse effects on their economic opportunities and social positions. Nevertheless, this article aims to provide a reference for governments, women’s organizations, and policymakers in assessing the implications of this pandemic for women and in designing sustained and targeted measures to support women vis-à-vis future crises.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.003 | 0.005 |
| 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".