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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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 teacher head, 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".