Using gender analysis matrixes to integrate a gender lens into infectious diseases outbreaks research
Bibliographic record
Abstract
Evidence shows that infectious disease outbreaks are not gender-neutral, meaning that women, men and gender minorities are differentially affected. This evidence affirms the need to better incorporate a gender lens into infectious disease outbreaks. Despite this evidence, there has been a historic neglect of gender-based analysis in health, including during health crises. Recognizing the lack of available evidence on gender and pandemics in early 2020 the Gender and COVID-19 project set out to use a gender analysis matrix to conduct rapid, real-time analyses while the pandemic was unfolding to examine the gendered effects of the coronavirus disease 2019 pandemic. This paper reports on what a gender analysis matrix is, how it can be used to systematically conduct a gender analysis, how it was implemented within the study, ways in which the findings from the matrix were applied and built upon, and challenges encountered when using the matrix methodology.
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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 | 0.030 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.006 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".