Price transmission with sparse market information: The case of United States chickpeas
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In this paper, supply‐related and price data for several chickpea importing regions and supply‐related data for export competitor countries were encompassed into price transmission models to determine whether such factors are helpful for explaining the United States chickpea price variation. Specifically, the models included satellite‐based normalized difference vegetation index, a measure of growing conditions, and area planted estimates for importing regions of the Indian subcontinent and the Mediterranean, and area planted estimates for exporting competitors, Australia and Canada. Results show that inclusion of supply‐related information improved goodness of fit statistics in all models relative to base models that only include prices, providing evidence that such information is important for explaining United States chickpea price changes. The models with the highest goodness of fit statistics were those that accounted for (1) deteriorating growing conditions in the Mediterranean region coinciding with declining area planted in Canada and (2) concurrently deteriorating growing conditions and declining area planted in the Indian subcontinent. [EconLit citations: Q11, Q17]
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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 | 0.001 |
| 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