EXPORT AND CULTURAL AFFINITY RELATIONSHIP: THE EXAMPLE OF RUSSIA (2001-2018)
Bibliographic record
Abstract
The aim of the study is to investigate the export of Russia to Lithuania, Latvia, Estonia, Georgia, and Ukraine in the context of cultural affinity. Lithuania, Latvia, Estonia, Ukraine, and Georgia are the former USSR (Union of Soviet Socialist Republics) countries. Besides, a quarter of the population is Russian origin in Latvia and Estonia, this rate is 6% in Lithuania, and 1.5% in Georgia. Therefore, there is a cultural affinity among Lithuania, Latvia, Estonia, Ukraine, Georgia, and Russia. In the study, the panel data analysis method and panel gravity model are applied. This analysis involves the years from 2001 to 2018. As a result, it is determined that if the populations and GDPs of Lithuania, Latvia, Estonia, Ukraine, and Georgia increase, they prefer not to trade with Russia. In other words, the cultural and historical relationships of Lithuania, Latvia, Estonia, Ukraine, and Georgia with Russia does not affect commercial relations positively.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".