Cultivating Equality: Economic Empowerment of Women and Sustainable Agriculture in Kenya
Bibliographic record
Abstract
This research article evaluates women's economic empowerment in agriculture in Kenya. The study employed the pro-WEAI (Women’s Empowerment in Agriculture Index) tool and data gathered from 422 households, 9 key informants, 6 focus groups discussions, and 6 case studies. The analysis encompassed five key agricultural domains as per the WEAI: community leadership, time use, resources production and income, Additionally, it explored decision-making autonomy, nutritional aspects, attitudes towards domestic violence, intra-household relationships and physical mobility. The article’s results highlight the existence of socio-economic and cultural barriers that hinder the participation of women and their success in agriculture, in particular, limited land ownership, finance and training, exclusion from decision making and access to information. The specific nature and intensity of these challenges can differ due to the unique socio-economic, cultural, and environmental context in Kenya. The study recommends the need to establish social interventions that address gendered power as a relational phenomenon within households, markets and value chains using gender transformative approaches, the need to develop a framework that recognizes and compensates equitably women’s contribution to agricultural, and unpaid care and other invisible domestic tasks in Kenya; engage with other key stakeholders including the public and private sector to facilitate women's access to markets, to mention just few.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".