More than a public health crisis: A feminist political economic analysis of COVID-19
Bibliographic record
Abstract
Gender norms, roles and relations differentially affect women, men, and non-binary individuals' vulnerability to disease. Outbreak response measures also have immediate and long-term gendered effects. However, gender-based analysis of outbreaks and responses is limited by lack of data and little integration of feminist analysis within global health scholarship. Recognising these barriers, this paper applies a gender matrix methodology, grounded in feminist political economy approaches, to evaluate the gendered effects of the COVID-19 pandemic and response in four case studies: China, Hong Kong, Canada, and the UK. Through a rapid scoping of documentation of the gendered effects of the outbreak, it applies the matrix framework to analyse findings, identifying common themes across the case studies: financial discrimination, crisis in care, and unequal risks and secondary effects. Results point to transnational structural conditions which put women on the front lines of the pandemic at work and at home while denying them health, economic and personal security - effects that are exacerbated where racism and other forms of discrimination intersect with gender inequities. Given that women and people living at the intersections of multiple inequities are made additionally vulnerable by pandemic responses, intersectional feminist responses should be prioritised at the beginning of any 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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.020 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".