Exploring Women’s Differentiated Access to Climate-Smart Agricultural Interventions in Selected Climate-Smart Villages of Latin America
Bibliographic record
Abstract
Much of the literature examining the role of gender in processes of climate change adaptation in the agricultural sector has focused primarily on differences between male and female farmers, implicitly treating men and women as homogenous groups. Where heterogeneity exists within these groups which impacts climate change adaptation efforts and outcomes, an understanding of such intersectionalities is vital to the design of effective and equitable policy. The objective of this study is to investigate whether interaction effects among socio-economic factors are meaningful drivers of observed differences among female farmers in their adoption of climate-smart agricultural (CSA) practices, as well as their use of climate information and financial services. This study employs data from farmer surveys in three Climate-Smart Villages in Latin America, analyzed using ordinal logistic regression and canonical correspondence analysis. The results indicate that important interaction effects are present: the relationship between higher educational attainment and increased adoption of CSA practices, for example, is conditional on the degree of livelihood diversification. The relationship between greater educational attainment and increased use of climate forecasts is likewise conditional on age. These results suggest the need for researchers and policymakers to anticipate potential intersectionalities when designing research efforts and development interventions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".