Women, livestock ownership and markets: Bridging the gender gap in eastern and southern Africa
Bibliographic record
Abstract
This book provides empirical evidence from Kenya, Tanzania and Mozambique and from different production systems of the importance of livestock as an asset to women and their participation in livestock and livestock product markets. It explores the issues of intra-household income management and economic benefits of livestock markets to women, focusing on how types of markets, the types of products and women’s participation in markets influence their access to livestock income. The book further analyses the role of livestock ownership, especially women’s ownership of livestock, in influencing household food security though increasing household dietary diversity and food adequacy. Additional issues addressed include access to resources, information and financial services to enable women more effectively to participate in livestock production and marketing, and some of the factors that influence this access. Practical strategies for increasing women’s market participation and access to information and services are discussed. The book ends with recommendations on how to mainstream gender in livestock research and development if livestock are to serve as a pathway out of poverty for the poor and especially for women.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| 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".