Beyond a Zero-Sum Game: How Does the Impact of COVID-19 Vary by Gender?
Bibliographic record
Abstract
Epidemics and pandemics, like COVID-19, are not gender neutral. Much of the current work on gender, sex, and COVID-19, however, has seemed implicitly or explicitly to be attempting to demonstrate that either men or women have been hardest hit, treating differences between women and men as though it is not important to understand how each group is affected by the virus. This approach often leaves out the effect on gender and sexual minorities entirely. Believing that a more nuanced approach is needed now and for the future, we brought together a group of gender experts to answer the question: how are people of different genders impacted by COVID-19 and why? Individuals working in women’s, men’s, and LGBTQ health and wellbeing wrote sections to lay out the different ways that women, men, and gender and sexual minorities are affected by COVID-19. We demonstrate that there is not one group “most affected,” but that many groups are affected, and we need to move beyond a zero-sum game and engage in ways to mutually identify and support marginalized groups.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".