Cattle as technological interventions: The gender effects of water demand in dairy production in Uganda
Bibliographic record
Abstract
Smallholder dairy production dominates the country of Uganda, with over 90% of the national herd owned by smallholders. To reduce hunger, malnutrition, and raise families out of poverty agricultural development, interventions in Uganda have focused on increasing milk production through the introduction of improved dairy cow breeds. Development actors, such as the East Africa Dairy Development (EADD) program in Uganda, see crossbreed dairy cows as a key technological intervention for improving production. Drawing on a multi-method study (spatial analysis, surveys, and qualitative interviews) of dairy smallholders, our paper examines the gendered effects of the introduction of crossbreed dairy cows. To ensure peak performance, improved breeds require more inputs (e.g., water, feed, and medicine), which are labor and time intensive with specific gendered outcomes. Our findings reveal that both men and women identify fetching water as one of the greatest challenges in maintaining dairy cows, but women and children disproportionately fetch the water and women have higher reported rates of time poverty. Water quality is also an issue, with smallholders struggling to provide clean water to cows, and our basic water testing reveals water sources with high nitrate levels that can be harmful for children and dairy cows.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".