The Prospects and The Competitiveness of Textile Commodities and Indonesian Textile Product in the Global Market
Bibliographic record
Abstract
This study has two objectives: first, to test the competitiveness of Textile Commodities and Indonesian Textile Product (TPT) in the global market and identify the prospects of the new export markets. Second, identify the competitiveness of the textile industry using case studies in the Solo Raya region. The Revealed Comparative Advantage (RCA) and Export Product Dynamics (EPD) methods are using in this study. The results show that Indonesian TPT commodities have a lost opportunity category in the central export destinations countries, such as a decline in market share. Indonesian TPT commodities have prospects in Austria, Canada, Finland, Norway, Portugal, Qatar, and Sweden due to competitiveness and domination in the market. Besides, the condition of the Indonesian textile industry competitiveness shows low competitiveness in terms of factor conditions, demand conditions, supporting and related industries, strategy, structure, and competition that are components of Porter's diamond model.JEL Classification: L6, L67How to Cite:Prasetyani, D., Abidin, A. Z., Purusa, N. A., & Sandra, F. A. (2020). The Prospects and The Competitiveness of Textile Commodities and Indonesian Textile Product in the Global Market. Etikonomi: Jurnal Ekonomi, 19(1), 1 – 18. https://doi.org/10.15408/etk.v19i1.12886.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".