MODELING WORLD PEANUT PRODUCT MARKETS: A TOOL FOR AGRICULTURAL TRADE POLICY ANALYSIS
Bibliographic record
Abstract
This paper presents a new partial-equilibrium, multi-market international model developed to analyze policies affecting peanut products markets. The model covers four goods (food-quality peanuts, crush-quality peanuts, peanut oil, and groundnut cake) in 13 countries/regions (Argentina, Canada, China, the EU-15, the Gambia, India, Malawi, Mexico, Nigeria, Senegal, South Africa, the United States, and Rest of World). Welfare is evaluated by looking at consumers' equivalent variation, quasi-profits in farming (peanut farming, livestock), quasi-profits in crushing, and taxpayers' revenues and outlays implied by distortions. We calibrate the model for three recent years (1999/2000, 2000/01, and 2001/02) on historical data. We illustrate the model's applicability with a peanut trade liberalization scenario. The impact of the reform scenario is measured in deviation from the historical baseline and by averaging the three estimates of annual impacts.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".