Championing gender in agricultural services in Kenya
Bibliographic record
Abstract
Key messages: • Champion farmers are male and female influencers recruited to support the delivery of agricultural services to fellow farmers within their communities (including seeds, advisories, and crop insurance), thereby promoting gender and social inclusion. • Providing insurance as a stand-alone product is too expensive to build a sustainable and cost-effective champion farmer model; there is a need to integrate the model with other services, including the provision of seeds, and to leverage government subsidies. • Champion farmers face steep competition from other service providers in the provision of seeds, but their networks give them opportunities to tap into underserved markets, as they have connections with women-led farmer collectives. • Female champion farmers’ socially ascribed gender roles and responsibilities related to homecare contribute to time poverty and drudgery and potentially inhibit the extent to which women can benefit from their champion role. • It is necessary to promote a better understanding of insurance among farmers and build farmers’ trust in services and products through additional training of champion farmers, sensitization of farmers, and awareness creation.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.033 | 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".