Finding opportunity in the COVID-19 crisis: prioritizing gender in the design of social protection policies
Bibliographic record
Abstract
The COVID-19 pandemic is highlighting the harm perpetuated by gender-blind programs for marginalized citizens, including sexual and gender minorities (SGMs) and cisgender women. Gender-blind programs are known to augment harms associated with violence and structural stigmatization by reinforcing rather than challenging unequal systems of power. The intersecting marginalization of these populations with systems of class, race, and settler-colonialism is exacerbating the impact that policies such as physical distancing, school closures, and a realignment of healthcare priorities are having on the wellbeing of these populations. The overarching reasons why women and SGM are marginalized are well known and stem from a hegemonic, patriarchal system that fails to fully integrate these groups into planning and decision making regarding public health programming-including the response to COVID-19. In this perspective, we aim to highlight how the exclusion of cisgender women and SGM, and failure to use a gender redistributive/transformative approach, has (i) hampered the recovery from the pandemic and (ii) further entrenched the existing power structures that lead to the marginalization of these groups. We also argue that COVID-19 represents a once-in-a-century opportunity to realign priorities regarding health promotion for cisgender women and SGM by using gender redistributive/transformative approaches to the recovery from the pandemic. We apply this framework, which aims to challenge the existing power structures and distribution of resources, to exemplars from programs in health, housing, employment, and incarceration to envision how a gender redistributive/transformative approach could harness the COVID-19 recovery to advance health equity for cisgender women and SGM.
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 | 0.004 | 0.001 |
| 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) | 0.000 | 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".