Gender dynamics in the biofortified bean value chain in Burundi
Bibliographic record
Abstract
Biofortified beans are being promoted in Burundi to solve malnutrition issues among rural households. The study was conducted in Muyinga and Gasorwe communes, where biofortified bean varieties were disseminated. This study aims to understand gender roles and practices in households and farms and how these roles and practices influence participation and uptake of biofortified beans. Lastly, it looks at the role of extension in increasing the uptake of agricultural technologies like biofortified bean varieties. Data were collected using a mixed-method approach—focus group discussions, key informant interviews and questionnaires and analyzed using content analysis, descriptive statistics, and a probit model on STATA software. Descriptive results indicated increasing joint farm management and decision-making on bean production, with men taking the lead in markets and income accrued from the sale of crops. Despite the promotion of various improved bean varieties, only two biofortified bean varieties, MAC44 and RWR2245, were adopted by smallholder farmers. The probit results indicated that utilization of hired labor, the source of biofortified improved bean seed, total cultivated land area, decision-maker on land use, years after the first adoption, and the type of extension services sought were significant factors that influenced farmers’ use of biofortified beans varieties. Based on the study's findings, more effort is required to promote access to inclusive extension services, market and decision-making on income accrued from the sale of farm produce.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".