Analisis Kinerja Ekspor dan Faktor-Faktor Yang Mempengaruhi Nilai Ekspor Tembakau di Kabupaten Jember Tahun 2005.I – 2009.IV
Bibliographic record
Abstract
This study emphasizes on how the export performance of a commodity in the international \nmarket when compared with the export of other commodities and also export similar \ncommodities from other countries. \nIn this study, the analysis of export performance of the approach used is the \nRevealed Comparative Advantage Index (IRCA). IRCA model used in this study is the \nadjustment of the model IRCA inherited a country in a region IRCA. In addition, also used \nmultiple linear regression (Multiple Regression Model). \nBased on the results of data analysis and discussion, it was found that the \ndevelopment of tobacco export performance in Jember regency over the past five years in \nthe periodization of the quarter using the Revealed Comparative Advantage Index (IRCA) \nshows the results fluctuate. While the regression results indicate that (a) the exchange rate a \nsignificant positive effecton the value of tobacco exportsin Jember district, (b) a significant \nnegative effectof inflation on the value of tobacco exports in Jember district, and (c) a \nsignificant positive effect of export volumes to the value of tobacco exports in Jember.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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".