Gender and intersectional analysis of livestock vaccine value chains in Kaffrine, Senegal
Bibliographic record
Abstract
Among livestock species, poultry and small ruminants are of particular importance to rural women in low- and middle-income countries, as means to generate income, provide nutritious food for the family, accumulate wealth, and confer social status. Newcastle disease (ND) and Peste des Petits Ruminants (PPR) are widespread livestock diseases of poultry and small ruminants, respectively. While both diseases are vaccine preventable, numerous constraints limit the availability of and access to livestock vaccines, especially among the most vulnerable populations in developing countries. The literature on equity and effectiveness of livestock vaccine distribution systems has emphasized many of these constraints, however a gendered analysis and deeper understanding of the vaccine system remain insufficient. This paper applies a gendered and intersectional transformational approach, or GITA, to highlight how gender and other social factors affect the provision and utilization of vaccines for ND and PPR diseases in the region of Kaffrine, Senegal. We first articulate and describe the vaccine value chains (VVCs) for these diseases in Kaffrine, and then analyze the gendered and intersectional dynamics at different nodes of the VVCs, including actors at the national level, through the regional and district levels, down to providers of animal health at community level and the livestock keepers themselves. Our findings indicate that actors' various experiences are shaped and defined mainly by rigid gender norms, location and remoteness, and to a lesser degree by other social stratifications of age, ethnicity, and livelihood. Given the significant role that gender norms play in the livestock vaccine value chains, differences according to the livestock species, regulation of vaccine administration, and vaccine distribution systems emerge as highly relevant for understanding barriers that women specifically face within the livestock vaccination system.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".