Bibliographic record
Abstract
Relevance of the research. Economic, financial, commercial and other relations are becoming faster in the global world. Business, trade relations with foreign investors, the optimal implementation of international relations in micro (company) and macro (country) level are important for producers and entrepreneurs. So it is relevant to carry out the scientific research in order to find out the optimal allocation of the world export according to volume of desired overall world export by using the mathematical modelling. Although the method of mathematical modelling is used in scientific research (e. g. Stonkienė, 2013; Radziukynas, Nemura, 2007.), no study was found where mathematical modelling would be used by the linear programming method and identifying the optimal export allocation, taking into account the conditions. So, this article complements a variety of research. The problem of the research: what is the optimal allocation of the world export between 11 countries when the volume of desired overall world export is minimum, medium or maximum? The object of the research is the allocation of the world export. The aim of the research is to identify the he optimal allocation of the world export between 11 countries (EU 28, Russia, Canada, the United States, Mexico, Brazil, China (except Hong Kong), Japan, South Korea, India, and Singapore) in 3 cases when the volume of desired overall world export is: 1) minimum; 2) medium; 3) maximum. The tasks of the research: 1.To present the methodology of the research. 2.To identify the he optimal allocation of the world export between 11 countries in 3 cases according to the volume of desired overall world export. 3.To summarize the main points of the allocation of the optimal world export and to submit recommendations. The research was carried out by using methods of case, comparative analysis and mathematical modelling applying the linear programming method. Eurostat statistical data of 2011–2015 were used for the mathematical modelling. Outcomes and conclusions. It was found out that EU 28, China and the United States are the same dominant countries in all three cases by the aspect of the world export volume. Moreover, the least volume of the world export is in India and Brazil. On the other hand, the differences between dominant countries which should have the biggest part of world export were found. China should have the biggest part of world export when the volume of desired overall world export is minimum and maximum. EU 28 should have the biggest part of world export when the volume of desired overall world export is medium.Keywords: international trade, export, mathematical modelling.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".