Gendered vulnerability and inequality: understanding drivers of climate-smart agriculture dis- and nonadoption among smallholder farmers in Malawi and Zambia
Bibliographic record
Abstract
In this study we explore gender-differentiated drivers of disadoption and nonadoption of climate-smart agriculture (CSA) technologies among smallholder farmers for everyday adaptation and resilience building in the face of the increasing threat of climate risk. We apply theoretical perspectives from mainstream technology adoption and gendered vulnerability to identify underlying vulnerabilities and inequalities that drive disadoption (the decision to discontinue any CSA technology previously practiced) and nonadoption (the decision not to use any form of CSA technology). We used an exploratory-sequential mixed methods design at the local level in Chikwawa, Malawi, and Gwembe, Zambia, to understand gender-differentiated drivers of CSA disadoption and nonadoption. Key interviews were conducted with identified critical informants at the district level, followed by focus group discussions with men and women at the village level to obtain qualitative data. We collected quantitative data through a cross-sectional household survey. Findings show that gender-differentiated drivers of CSA disadoption and nonadoption fall within social, economic, institutional, and environmental categories and underlying gendered vulnerability and inequality shape these drivers. CSA is introduced within preexisting gendered vulnerability and inequality, shaping adoption decisions by diverse groups of female and male smallholder farmers. Consequently, CSA outcomes of improved agricultural productivity, adaptation, and resilience building may not be equally achieved because of gender inequalities and vulnerabilities that demotivate diverse households from adopting CSA. This work contributes to a contemporary gender-transformative paradigm in climate change adaptation and disaster risk reduction by focusing on CSA adoption in climate-sensitive regions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 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".