Using Outcome Mapping to Mobilize Critical Stakeholders for a Gender Responsive Rift Valley Fever and Newcastle Disease Vaccine Value Chain in Rwanda
Bibliographic record
Abstract
Approximately 752 million of the world's poor keep livestock to produce food, generate income, and build assets. Women represent two-thirds (~400 million people) of low-income livestock keepers. Infectious diseases are a major issue in preventing livestock keepers from optimizing production earnings and improving food security. In Rwanda, highly contagious yet preventable diseases that affect animals that women manage, such as Rift Valley fever in goats and Newcastle disease in chickens have a high-mortality rate and can devastate their herds. Women are disproportionately affected because they bear primary responsibility for goats and chickens. These diseases are preventable through vaccination, but smallholder women farmers rarely benefit from livestock vaccines. Social norms and entrenched cultural stereotypes limit women's confidence and decision-making and restrict their access to resources and information. Women smallholder farmers find that there is little support for the small livestock they manage, because of the official preference given to cattle. They are also challenged by limited availability of livestock vaccines due to lack of a cold chain, inadequate extension, and veterinary services, especially for goats and chickens, and unreliable structures for vaccine delivery. To identify opportunities for women's engagement in the livestock vaccine value chain (LVVC) and reduce their barriers to accessing and using livestock vaccines, we used Outcome Mapping, a stakeholder engagement tool, and the Gender Equality Continuum Tool to classify and engage critical partners in the LVVC. We analyzed each critical partner's capacities, incentives, and drivers for engagement with women, challenges and barriers that hinder their support for women farmers, opportunities at systemic and programmatic levels for women's participation and benefit in the LVVC, and the gender capacities and perceptions of different stakeholders. Enhanced positioning and visibility of women in the LVVC can occur through a systemic engagement of all stakeholders, and recognition of the roles that women play. Women smallholder farmer involvement when determining and shaping the potential entry-points is critical to ensure support for their existing responsibilities in family food security, and future opportunities for generating income. Strengthening gender capacities of LVVC stakeholders, addressing identified barriers, and building on existing opportunities can increase women's participation in the LVVC.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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".