Bibliographic record
Abstract
Relevance of the research. The allocation of world export between countries, groups of countries is relevant not only at the theoretical level but at the practical level, too. Benefit of export is emphasized for both the economic growth of a country, development and individual entrepreneurs (Jatuliavičienė, 2009). The importance of the dynamic analysis of the World export can be justified on the basis of empirical research. For example, World Trade Organization (2015) analyses the key tendencies of international trade in 1995–2014, United Nations Conference on Trade and Development (2015) also investigates the key aspects of international trade, statistics, etc. In 2015, N. Halevi (2015) explores the characteristics of export, its volume, etc., between 20 OECD countries in 2007 and other research. Hence, it is important to continue the analysis of world export tendencies in the past, present and future. The object of the research is the world’s export. The problem of the research: how did the world’s export change in 11 countries, group of countries at pre-crisis, crisis and post-crisis periods and what are the future perspectives in the context of export? The aim of the research is to carry out the analysis of the world’s export dynamics in 11 countries (EU 28, Russia, Canada, the United States, Mexico, Brazil, China (except Hong Kong), Japan, South Korea, India, Singapore) in 2 aspects: (1) time (pre-crisis (2002–2007), crisis (2008–2010) and post-crisis (2011– 2014) periods); (2) countries, group of countries. Furthermore, the aim is to provide a forecast of the world’s export in 2015. In order to achieve the aim, we formulated 3 main tasks of the research: 1) to present the methodology of the research, providing study limitations; 2) to carry out the world’s export dynamic analysis and present the forecast of it; 3) to summarize the main points of the dynamic analysis identifying the potential directions for future research. According to previous studies (e.g. United Nations Conference on Trade and Development, 2015; World Trade Organization, 2015 et al.), this research is carried out by using two methods: comparative statistical analysis and forecast. The novelty of this research is related with the methodology of this research: the dynamic analysis is carried out in 2 ways: by the aspect of time (pre-crisis, crisis and post-crisis periods); (2) by the aspect of countries, group of countries. The secondary data of Eurostat database (2002–2014) were used in this article. Outcomes and conclusions. It was found that the volume of the export was decreasing in post-crisis period and in the future (2015). The opposite trend (export was increasing) was observed in pre-crisis period. On the other hand, the world’s export was increasing in EU 28, Russia, South Korea, India, China, Brazil in 2002–2014. Moreover, it was found out that the world’s export was decreasing in Canada, USA, Mexico, Singapore and Japan in 2002–2014.Keywords: international trade, export, pre-crisis, crisis and post-crisis periods, the forecast.
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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 | 0.010 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.045 | 0.011 |
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".