Potato Trading Based on Structure Conduct Performance (SCP) in the Centre of Vegetable Production at Central Java Indonesia
Bibliographic record
Abstract
This study aims to examine potato trading based on Structure, Conduct, Performance (SCP) in the highland vegetable production centers of Central Java Province. Potato trading in highland vegetable production centers in Central Java province has been analyzed using Structure, Conduct, and Performance (SCP) techniques. The data was collected through a survey and observation. The market structure was analyzed by market share, the Herfindahl-Hirschman Index (HHI) and the Concentration Ratio for Biggest Four (CR4). Analysis of market behavior includes the presence or absence of collusive practices in determining prices, the process of selling and buying, the formation of equilibrium prices, payment systems (cash, credit), and cooperation with other trading institutions. While market performance is analyzed by marketing margins and farmer's share. The samples of potato farmers were determined by the random sampling method and the traders determined by the snowball sampling method. The number of samples used was 82 potato farmers, 45 collecting traders, 10 wholesaling traders, and 14 retailing traders. The results of market structure research on potato trading are oligopsonies. Price behavior at farmers (producer) level is more controlled by collectors who deal directly with farmers. Wholesalers dominate purchases from collectors, the payments are made in cash or paid later. Moreover, the collusive practice between collectors and wholesalers occurs, especially in the provision of capital or credit. Furthermore, the performance of the potato market based on the trading system margin is greatest in pattern 2, while the farmer’s share is greatest in pattern 1 in the potato trading system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".