Studying a gender responsive vaccine system: retrospective analysis of best methods
Bibliographic record
Abstract
This methodological paper introduces four projects, all of which aimed to increase women’s engagement in and benefit from the livestock vaccine value chains of small ruminants and poultry by improving women’s empowerment and supporting women’s access to animal health services. All four projects used a mix of qualitative and quantitative research methods to understand the livestock vaccine system. Despite these shared aims, selected value chains, and research methods, the projects took different approaches to understanding the technical barriers for women’s engagement and benefit, women’s empowerment in the areas where they work, the policy landscape and implications, and gender norms of the societies where they work. The goal of this paper is to introduce the four projects, describe each project’s distinct research approach, and compare across projects how various qualitative and quantitative research methods contributed to understanding four elements which we identified as necessary for a fully functioning, gender responsive vaccine system: technical aspects (acumen/flow/effectiveness), women’s empowerment, policy environment, and gender norms.
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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 | 0.127 | 0.219 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".