Determinants and Potential of Indonesia’s Frozen Shrimp Exports
Bibliographic record
Abstract
This study examines the main factors influencing the flow of Indonesian frozen shrimp exports and explores opportunities to strengthen trade with key destination countries. The research uses a panel data regression approach, including estimation and potential analysis, focusing on 12 major trading partners from 2012 to 2023. The study analyzes frozen shrimp under HS code 030617, one of Indonesia's leading fishery export commodities. The findings show that importer gross domestic product, importer population, and revealed comparative advantage positively influence frozen shrimp exports. In contrast, Indonesia's gross domestic product, economic distance, and non-tariff barriers have a negative impact. Based on the regression results, the potential analysis identifies several countries with strong export prospects, including Australia, the Netherlands, Hong Kong, the United Kingdom, Japan, Germany, Canada, South Korea, Malaysia, France, and Singapore. These results provide meaningful managerial implications, such as increasing domestic production capacity, enhancing export competitiveness, and developing focused marketing strategies to penetrate high-potential markets.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".