ANALISIS DAYA SAING DAN FAKTOR DETERMINAN YANG MEMPENGARUHI EKSPOR UDANG INDONESIA
Bibliographic record
Abstract
Shrimp is the leading non-oil and gas export commodity as well as the Indonesian fishery sub-sector. Shrimp commodity is also a commodity that has high competitiveness because it has fluctuating exports in the period 2013-2020. The purpose of this study was to determine how the condition of competitiveness of Indonesian shrimp commodities and the influence of competitiveness, production, GDP per capita and foreign exchange rates ( US$) partially and simultaneously on Indonesian shrimp exports in five export destination countries (United States, Japan, China, Malaysia, Canada). The data used in this study is panel data. Panel data is a combination of time series and cross section. To analyze the competitiveness of Indonesian shrimp commodities in five destination countries, the RCA (Revealed Comparative Advantage) method was used. The RCA values ??for the five destination countries show that Indonesian shrimp exports have strong competitiveness. Based on the research results, competitiveness, production, GDP per capita and foreign exchange rates simultaneously have a significant influence on Indonesia's shrimp exports. Partially competitiveness, GDP and foreign exchange rates have a positive and significant impact on Indonesia's shrimp exports. Meanwhile, production has a negative and insignificant effect on Indonesian shrimp exports
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".