Postcards from the Pandemic: Women, Intersectionality, and Gendered Risks in the Global COVID-19 Pandemic
Bibliographic record
Abstract
Abstract The COVID-19 crisis created, and continues to produce, unprecedented challenges globally. Marginalized and racialized families, communities, and nations are experiencing their worst impacts, and in particular, women and girls are the hardest hit. The most pressing concerns raised by COVID-19 include a surge in gender-based violence, a rise in care burden, the feminization of poverty, and growing unemployment, largely in the Global South and conflict-affected regions. Drawing on feminist security studies and intersectionality literature, this forum explores gendered risks in the COVID-19 era, focusing on the security of women and girls from racialized and marginalized backgrounds in both the Global North and South. This forum presents seven short papers providing rich data on a range of case studies that include Yemen, Sri Lanka, Liberia, Canada, India, and Burundi. The contributions draw attention to the multilayered, diverse, intersectional, complex, and contextual gendered risks associated with the pandemic. The through line themes of intersectional identities, patriarchy, conflict, post-conflict, militarization, and marginalization are used to illustrate how gendered risks are (re)constructed during and after the COVID-19 crisis. This forum launched what we hope will offer a new research agenda and support to provide scholarly terrain for future research. This forum section not only provides insights into the vast and complex gendered impacts of the COVID-19 pandemic but also sparks broader thinking about everyday forms of insecurity that women and girls face in global 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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.011 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".