<i>Ex Ante</i> Welfare Analysis of Technological Change: The Case of Nitrogen Efficient Maize for African Soils
Bibliographic record
Abstract
This study evaluates the potential impacts of the Improved Maize for African Soils (IMAS) project in two countries of Africa: Kenya and South Africa. The IMAS varieties offer significant yield advantages for regions where low or no fertilizer is used. The analysis uses spatial production data and household data to account for the level of fertilizer use in different agroecological zones of the country as well as different types of maize producing households. Results suggest that IMAS will deliver a total of US$586 million in gross benefits with US$136 million and US$100 million of benefits to producers in Kenya and South Africa, respectively, and an additional US$112 million to consumers in Kenya and US$238 million to consumers in South Africa. These benefits could help more than 1 million people escape poverty in the two countries by 2025. Household level results suggest that small households in areas with relatively low levels of fertilizer use stand to gain significant benefits.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".