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
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.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.007 |
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