Gender norms and differences in access and use of climate-smart agricultural technology in Burundi
Bibliographic record
Abstract
The adoption and use of climate-smart agricultural practices are critical for improving the productivity and sustainability of smallholder farming systems. However, the gendered dimensions of access to and use of climate-smart agriculture in common bean (Phaseolus vulgaris L.) production remain unexplored among smallholder farmers in Burundi. A mixed methods research design was employed to investigate gender dynamics in common bean production among smallholder common bean farmers in the communes of Kirundo, Bwambarangwe, and Muyinga in Burundi. In addition, how the adoption and use of climate-smart agricultural practices differed by gender in Burundi. A multivariate probit model was employed to evaluate how improved bean seed, pesticide use, irrigation, conservation agriculture and other factors contribute to reducing gender gaps and influencing access to and uptake of climate-smart agriculture. The results revealed existing gender gaps and differences in access to and use of climate-smart agriculture practices, with women being the most vulnerable. Disproportionate experiences of production challenges emerged as critical obstacles to gender equality in bean production. Drought affected women and young farmers more severely than men. Joint decision-making, access to information, and collective action in groups reduced gender gaps in bean production and gender differences in access to and use of climate-smart technologies.
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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 | 0.002 | 0.002 |
| 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 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".