African women and young people as agriculture service providers—business models, benefits, gaps and opportunities
Bibliographic record
Abstract
Abstract We use a combination of a global desk review of the literature with information from an on-going action research in Kenya to provide insights into the main characteristics, benefits and shortfalls of business models for engaging women and young people in agricultural service provision in Africa. The findings demonstrate that the engagement of African women and young people in agricultural service provision is not a panacea to the challenges they face. However various business models have been successful in contributing to economic empowerment, to increasing entrepreneurial activities and to upskilling of women and young people engaged as service providers. Business models that are successful are place-based and people-focused, market-driven and focused on value chains. Challenges however abound due to various factors, hence for sustainability there is need for multi-sectoral inter-institutional collaboration that pulls in funding and which makes a case for private sector buy-in. Future research should focus on increasing the evidence base to understand if successes with inclusion of women and young people in agricultural service provision has an influence on emerging agricultural policy. Research should also rigorously assess the extent to which successful agricultural service provision business models are engendered, provide sufficient levels of renumeration and the extent to which they impact farmer outcomes.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".