Analisis Kinerja Ekspor dan Faktor-Faktor Yang Mempengaruhi Nilai Ekspor Tembakau di Kabupaten Jember Tahun 2005.I – 2009.IV
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it