Strategy of Developing Local Economy Based on Regional Superior Commodities
Bibliographic record
Abstract
This paper discusses the strategy of developing a local economy based on regional superior commodities. Not many people can to process superior commodities into derivative products that have added value. Also, the limited access to price information and marketing networks forces farmers to sell their crops to collectors at a price that is determined unilaterally, which is why farmers do not get the maximum benefit. The purpose of this study is to formulate a leading commodity-based local economic development strategy to create competitiveness to improve the people’s economy. The method used is descriptive with a quantitative approach and is supported by location quotient (LQ) analysis, Shift-Share, and Value Added. To formulate a strategy used a SWOT analysis, to determine the program carried out by comparing current conditions with desired conditions and referring to the results of the SWOT analysis. The results of the study show that leading commodities are proven to have comparative advantages and have the potential to become the basis of regional economies. The integrated commodity product processing industry that produces a variety of processed products can provide economic value that increases the final value of superior commodities. Also, the activities of processing derivative products are also able to produce added value, provide profit margins to workers and employers, as well as contribute to other inputs for each kilogram of product produced. Processed products have the potential to provide high price margins to farmers and producers if the marketing system is more efficient. Based on the analysis-analysis, industrial clusters based on regional superior commodities can be developed.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".