Intermarket Performance and Pricing Efficiency of Imported Rice Marketing in South-South Nigeria: The Case of Akwa Ibom State Traders
Bibliographic record
Abstract
The paper examines the market performance and pricing efficiency of rice uses primary data from a sample of 60 rice traders selected from four markets in Akwa Ibom State, Nigeria. Data were analyzed using simple descriptive statistics, t-test, gross margin and bivariate correlation coefficient. From the findings of the study, married (70%) and educated (85%) female (63.3%) rice traders with average marketing experience of 11.6 years dominated the study area. Average Gross return and margin across the markets were #8852.5 and 27.67%, indicating that rice marketing was profitable in the study area. Rice prices were higher in the rural than the urban markets. The correlation coefficient between the urban market pair was higher (0.81) than those between the urban and the rural market pairs which ranged from 0.21 to 0.46.This shows that the flow of marketing information was higher among the urban market pairs and lower between the rural and urban market pairs. This implies that the urban market pair was highly integrated than the other market pairs that were poorly integrated. There were significant differences in the mean wholesale prices between the urban and rural market pairs as well as within the rural market pair, while there were no significant differences in the mean wholesale prices between the two urban market pair. Also, high cost of transportation, high rent and taxes, lack of credit facilities and rampant incidence of theft were among the perennial marketing problems identified as major challenges in the study area. Series of recommendations have been offered.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".