Breaking institutional barriers to enhance women’s participation in and benefit from the Peste des Petits Ruminants and Newcastle Disease vaccine value chains for Sembabule district of Uganda
Bibliographic record
Abstract
This paper describes the institutional context that shapes the visibility and positioning of women along the Peste des Petits Ruminants (PPR) and Newcastle Disease (ND) vaccine value chains for Sembabule district of Uganda. It examines the institutional barriers and opportunities that affect women's empowerment derived from inclusion of women in the decision-making processes along the livestock vaccine value chain (LVVC) and that can support viable women-centered and owned enterprises, at the vaccine development, delivery, distribution and use level. Qualitative data analysis tools such as focus group discussions, focus meals, jar voices and key informant interviews were used. Using outcome mapping, a stakeholder analysis of the critical partners in the PPR and ND value chain was done involving the regulators, vaccine manufacturers, importers, distributors, agrovets, public and private veterinary service deliverers, local leaders, women groups, and farmers. The study concluded that training related to gender equality and livestock vaccines, infrastructural and technical support to the poultry and goat women and men farmers and other chain actors are inadequate in themselves to increase vaccine adoption and improve livestock productivity in Sembabule district. Strategies that promote gender-transformative collaborative efforts among the LVVC actors and build viable gender-transformed women groups and networks are critical to increase women's participation in and benefit from the livestock vaccine value chain.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".