Prospects for the Development of Russian Export in the Context of Digitalization
Bibliographic record
Abstract
The global trend in digitization has revolution the global economy and the way of doing business in our world currently. The digital trend is instrumental to globalization and specifically international trade. In Russia, the application of digitization is relatively low compared to other emerging economies. Therefore, it becomes interesting to assess the prospects of digitization in increasing export of the country and extensively, if such influence on export is industrial sensitive. To accomplish this, we assessed industrial export of Russia and used panel Autoregressive Distributed Lag (ARDL) technique to determine the impact of digitization on Russia’s export. By implementing Mean Group (MG) estimator which was adjudged to be suitable for this model through the Hausman test, it could be revealed that the impact of digitization is more intense in the short-run. The long-run effect is not statistically significant. Based on industries, digitization is significantly responsible for the export of Crude materials, inedible, except fuels; and Machinery and transport equipment in the short-run while also contributes to the long-run increase in export of Beverages and tobacco. The prospect can be increased when the country adapts and adopts more in the global trend.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".