Designing a food supply chain network under public-private community partnership on traditional Indonesia markets
Bibliographic record
Abstract
This study examines the effect of the performance of traders in the partnership relationship between the government, the private sector, and the community which is very necessary to build a food supply chain network in order to provide stimulation for the supporting factors and reduce the inhibiting factors for the success of the merchant's business, increase profits, and maintain food availability in traditional markets. This research was conducted at Andir Market, Bandung City, Indonesia. The research sample consisted of 100 Andir market traders. The research method used was a quantitative method with survey research that uses a questionnaire as the main instrument in data collection. The results of the analysis and discussion of the researchers have identified factors that can be used to strengthen the food supply chain network in the Public-Private-Community Partnership in traditional markets, namely management, empowerment, physical market conditions, and competitive strategies. Simultaneously, the four traders' performance factors have a significant effect on the food supply chain network with an R-square value of 0.658 (65.8%) with tcount greater than 1.96 or (3.817> 1.96). This means that if the changes that occur are good in the performance of traders, the food supply chain network in traditional markets will also be good, while the remaining 0.342 (34.2%) is explained by factors other than these variables. The expected implication is that the success of the supply chain network that is built due to strong interactions within the Public-Private-Community Partnership and other supply chain actors is able to face challenges during and after the pandemic. So that the quality of traditional market products, especially products that are quickly damaged or rotten, can be superior and well guaranteed for the food needs of people throughout Indonesia.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".