Climate change adaptation and productive efficiency of subsistence farming: A bias‐corrected panel data stochastic frontier approach
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We explore the impact of climate change adaptation on the technical efficiency of Ethiopian farmers using panel data collected from 6820 farm plots. We employ Green's (2010) stochastic frontier approach and propensity score matching to address selection bias. Our results reveal that climate change adaptation improves the efficiency of maize, wheat and barley production. We also show that failure to account for selection bias underestimates the average efficiency level. Our findings imply that the expansion of climate change adaptation at larger scales will provide a double benefit by curbing climate‐related risks and increasing the efficiency of farmers. Moreover, increasing credit access and introducing mechanisms that allow farmers to get enough water during the main growing season will enhance the efficiency of subsistence farmers.
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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 | 0.001 | 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