Characteristics of foreign trade between the Serbia and Russian Federation
Bibliographic record
Abstract
Many studies proves that all economies have had high rate of economic growth in the long run (increase of 7% for more than two decades) that was without any exceptions export-driven. Analysis of foreign trade between the Serbia and Russian Federation should reveal segments in foreign trade where amount traded should be intensified. Two decades ago Russia was the most important Serbian foreign trade partner, where Serbia had foreign trade surplus. Today Serbian export to Russian Rederation covers onlz quarter of its import, despite the fact that two ceountries signed Treatment on foreign trade in August 2000. In the first chapter of the paper we analyze Serbian economic structure, due to the fact that economic structure deteminate export. The new model of Serbian economic growth should be based on tradeble economic sectors. Russian model of economic development, that had been significantly changed during hte last two decades in transition from planned to market economy, is subject of analysis in the second chapter. In the third chapter of the paper Serbian-Russian Federation foreign trade is analysed in order to discover which market segments could be important for Serbian exporters.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".