Seed credit model in Uganda”: Participation and empowerment dynamics among smallholder women and men farmers
Bibliographic record
Abstract
Seed is life and can be a source of empowerment and disempowerment for women and men farmers. In this study, to close the gender gaps in seed, the Community Enterprises Development Organization, the Alliance of Bioversity and CIAT and the National Agricultural Research Organization developed a seed credit model available to men and women belonging to farmer groups. A mixed method was used to collect information from two districts in central Uganda on how the seed credit model reconstructed access, use, control and resulting benefits. Results showed that the provision of the seed credit model was considered a blessing even though it had many nuances. As a result of the seed credit model, we saw increased productivity in women's fields, increased income and decision making over income incurred from the sale of their crops. Their social status has been enhanced, and they now occupy a place of respect in their communities and households, where they can make decisions and get assets like houses and land. While it increased productivity, income and enhanced food and nutrition security needs of the family, it also changed power dynamics within the household as women become more empowered. To maintain power relations, men limited women's access to fertile land and family labor, which defined the quantity of seed gotten from the seed credit model. Women's participation and involvement in the seed credit model decreased over time as they were expected to pay their spouses' seed loans. Men's participation decreased because they were no longer entrusted with seed loans as their payment rate was very low. As we reap positive benefits, we have to ensure we don't 'do harm' when empowering our beneficiaries.
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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 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".