Climate smart agricultural practices and gender differentiated nutrition outcome: An empirical evidence from Ethiopia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Since the beginning of the decade, climate resilient green economy strategies have been proposed in many African countries. One of the pillars of the strategies is the adoption and diffusion of various climate smart agricultural practices for improving crop and livestock production and farmer income while reducing greenhouse gas emissions. The effects of these innovations on household nutritional security, including gender-differentiated nutritional status, have hardly been analyzed. We examine the determinants of adoption of combinations of multiple climate smart agricultural innovations and their impact on different nutrition outcomes. We find that adoption of climate smart innovations increases dietary diversity and improves calorie and protein availability. These benefits increase with adoption of combinations of innovations, relative to adopting an innovation in isolation. Gender-disaggregation results suggest nutritional outcome differentials between male and female headed households due to both differences in household characteristics, including household resources, and differences in returns to resources. The study provides insight into the interaction between climate change adaptation and nutrition security among male and female headed households, with implication for the Sustainable Development Goals of ending hunger, achieving gender equality, and taking action on climate change.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it