Cobweb Model with Buffer Stock for the Stabilization of Tomato Prices in Ghana
Bibliographic record
Abstract
In this paper a linear cobweb model is developed to study the phenomenon of commodity price fluctuations and then a buffer stock incorporated into the model to stabilize the price of fresh tomatoes in Ghana. The model performed on the assumptions that fresh tomatoes have no equal substitutes, and that there is no foreign competition and also no exogenous shocks needed to generate price fluctuations. The analysis detected that the slope of the demand function of price was smaller than the slope of the supply function of price curve implying that the price and quantity supplied of the fresh tomatoes would oscillate around a fixed price and quantity and also spiral outward. The “Keep Supply at Average” (KSA) buffer scheme achieved price and quantity stability in the short run. The mean price of the scheme was GH¢17.31, very close to actual price mean of Gh¢ 13.40 in the first 16 quarters. The standard deviation of the scheme price also dropped to 1.2 from 9.13 during price stabilization compared to 14.60 of actual price mean. In the long run the scheme price went up to Gh¢ 18.22, an increase of Gh¢ 0.91 and it is clear that in long run buffer system will fail unless the average supply is reviewed regularly. The scheme price trend equation indicated that with the implementation of the buffer scheme, the average quarterly price of fresh tomatoes increased by only 0.05.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".