A Microeconometric Analysis of Adapting Portfolios to Climate Change: Adoption of Agricultural Systems in Latin America
Why this work is in the frame
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Bibliographic record
Abstract
Abstract This paper develops a microeconometric analysis of adapting portfolios in response to climate change using information on South American farmers' adoption of agricultural systems taken from approximately 2,000 household surveys. The results show that farmers in a hotter climate prefer a mixed system over specialized systems either in crops or livestock. Under a hot and dry scenario, the land values of all three systems would fall, but the damage would be much smaller in the mixed system (−10%) than for the farms specializing in crops (−20%). Farmers are predicted to lose 8% of their land's value under the Canadian Climatic Center (CCC) scenario, but only 2% under the Parallel Climate Model (PCM). Losses would increase to 18% if they do not adapt.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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