Bibliographic record
Abstract
The main goal of this paper is to show the level of globalization, its changes and the impact of globalization on economic growth and socio-economic development in these countries. The following research methods were used: historical, literature, descriptive analysis and simple statistical methods. Statistical data used in this paper come from KOF Index of globalization, World Bank Database and Human Development Reports. The time range of research is 1990-2018. The research covered 16 countries of Western Europe, USA, Canada, Japan, Australia and New Zealand. The main findings of the study are as follows: Highly developed countries are the most globalized. The level of globalization in individual countries varies, but the differences are not large. The medium-size European countries are the most globalized, while non-European countries are the least globalized. Starting from the 1990s, the level of globalization has increased significantly. The highest increase was in the less globalized countries, the lowest in the most globalized ones. As a result, the differences between them have significantly decreased. Thus we can see the convergence in the sphere of globalization. The positive impact of globalization on economic growth and socio-economic development was not observed in this group of countries.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".