An opportunity for gender transformation? UN Women’s policy response to COVID-19
Bibliographic record
Abstract
Pandemics disproportionately affect women due to their dominant roles in healthcare, caregiving, and industries vulnerable to public health policies. Women face higher infection risks, greater unpaid care burdens, and job losses during crises. Violence against women and disrupted access to healthcare, including sexual and reproductive services, also increase. Despite clear evidence of these effects, global pandemic responses have historically been gender-blind, with only limited improvements during COVID-19. This study uses the READ approach to analyze UN Women COVID-19 policy documents published in 2020, examining recommendations related to socio-economic security, violence against women and girls (VAWG), and people living across borders. From these documents we also analyzed 301 recommendations using the WHO's Gender Responsive Scale to assess their transformative potential. The results show that while UN Women addressed key gendered impacts, the recommendations often stopped short of promoting systemic change, reflecting broader limitations in global health responses. The findings highlight the gap between acknowledging gender disparities and promoting (let alone implementing) transformative policies that address structural inequalities. This research contributes to ongoing debates on the role of global institutions in advancing gender-responsive pandemic policies and calls for more meaningful engagement in addressing gender inequities in global health governance.
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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 | 0.008 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 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".