Time for gender mainstreaming in editorial policies
Bibliographic record
Abstract
The HIV epidemic has been continuously growing among women, and in some parts of the world, HIV-infected women outnumber men. Women's greater vulnerability to HIV, both biologically and socially, influences their health risk and health outcome. This disparity between sexes has been established for other diseases, for example, autoimmune diseases, malignancies and cardiovascular diseases. Differences in drug effects and treatment outcomes have also been demonstrated. Despite proven sex and gender differences, women continue to be underrepresented in clinical trials, and the absence of gender analyses in published literature is striking. There is a growing advocacy for consideration of women in research, in particular in the HIV field, and gender mainstreaming of policies is increasingly called for. However, these efforts have not translated into improved reporting of sex-disaggregated data and provision of gender analysis in published literature; science editors, as well as publishers, lag behind in this effort.Instructions for authors issued by journals contain many guidelines for good standards of reporting, and a policy on sex-disaggregated data and gender analysis should not be amiss here. It is time for editors and publishers to demonstrate leadership in changing the paradigm in the world of scientific publication. We encourage authors, peer reviewers and fellow editors to lend their support by taking necessary measures to substantially improve reporting of gender analysis. Editors' associations could play an essential role in facilitating a transition to improved standard editorial 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 | 0.161 | 0.400 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.034 | 0.023 |
| Open science | 0.008 | 0.007 |
| Research integrity | 0.032 | 0.048 |
| Insufficient payload (model declined to judge) | 0.019 | 0.015 |
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".