Competitiveness and the Factors Affecting Export of the Indonesia Canned Pineapple in the World and the Destination Countries
Bibliographic record
Abstract
Indonesia has a comparative advantage as the largest pineapple exporter in the world. Most of the pineapples are exported in the form of canned pineapples. This study examines the competitiveness of Indonesian exports of canned pineapple in the world and in the destination countries by using the method of Revealed Competitive Advantage (RCA), Export Product Dynamics (EPD), Intra-Industry Trade (IIT), and a panel data regression analysis approach through E-views 6 for the period 2004 until 2013. RCA analysis results indicate that the Indonesian canned pineapple has a comparative advantage in the world as well as in the export destination countries. EPD analysis results indicate that the Indonesian canned pineapple has a highly competitive advantage by positioning a rising star in the world and in the seven export destination countries, including the United States, Spain, Italy, Canada, Denmark, Austria, and China. IIT analysis results indicate that Indonesia has a one-way trade flows and a lower degree of integration towards export destination countries. Finally, the results of panel data analysis indicate that Factors that affect the export volume of Indonesia canned pineapple in the destination countries are Indonesia canned pineapple export prices to the export destination countries, real GDP and the population of destination countries Keywords: Competitiveness, RCA, EPD, IIT, Panel Regression
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".