Lessons From Zika Policies to Improve Gender Equity
Bibliographic record
Abstract
Gender equity is easily supported in theory but harder to pursue in practice. In this article, the case of Zika travel policies is used to illustrate some glaring gaps related to gender, for both men and women, at both international and national levels. Zika travel policies have not considered new evidence on biological or social determinants of health, putting babies at risk of exposure. The authors suggest best practices at the international level, such as developing pre-organized gender committees to provide actionable and swift advice for international infectious disease policies; at the national level, such as promoting holistic policies addressing mosquito control and sex and gender considerations, including access to reproductive health services; and at the local level, such as education on local infectious diseases. These deliberations are especially important with emerging infectious diseases (EIDs), as little may be known about them. New knowledge needs to be translated in a timely fashion in order to shape effective and equitable policies.
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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 | 0.013 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.009 | 0.011 |
| Insufficient payload (model declined to judge) | 0.013 | 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".