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 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 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".