“Downstreaming” Policy Supporting the Competitiveness of Indonesian Cocoa in the Global Market
Bibliographic record
Abstract
Indonesia is one of the cocoa producing countries, where most of it is exported to foreign countries and the rest is marketed domestically. Indonesia cocoa export performance in the world market certainly opens up many opportunities. It is necessary to optimize the potency and competitiveness of its cocoa if Indonesia would make the cocoa exports as the driving of national economy. The objectives of this paper are to (1) analyze Indonesian cocoa performance in the global market compared to its competitors and (2) analyze the competitiveness and market position of Indonesian cocoa in global market and analyze the potency to develop market in 10 main trading partners. The data analysis methods used are the Revealed Comparative Advantage (RCA), Trade Specialization Index (TSI) and Export Product Dynamics (EDP). The result shows that the comparative competitiveness of Indonesian cocoa beans and processed cocoa is lower than that of other producing countries. However, Indonesia still has the potency to develop market for its cocoa products in several countries such as the United States, China, India, Canada, Mexico and Estonia. Some efforts to improve the competitiveness of Indonesian cocoa beans may be through the replanting for estates rejuvenation and the improvement of fermentation to improve the quality of cocoa beans. In addition, to enhance the export performance of cocoa base products in general, it is necessary to also improve the development of downstream line and processing industries.
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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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".