Ways to enhance the competitiveness of Russia in an unstable world economy
Bibliographic record
Abstract
© 2014, Canadian Center of Science and Education. All rights reserved. Despite the recent positive trends in the Russian economy, the problem of increasing national competitiveness today. With the acceleration of globalization of economic relations and deepening of the international division of labour, a priority is to ensure an adequate level of competitiveness of the Russian economy. The dynamics of trends of the economic situation (world and Russian) reiterates the need to continue the implementation of institutional reforms in the country, the establishment of a new system of socio-economic relations, development of market mechanisms for deepening mutually beneficial cooperation with foreign States. For the formation of new conditions for sustainable development of the world economy should be significant structural changes in the technological and organizational nature, both at the international and at the national level. The high level of uncertainty in the economy of modern Russia leads to two main outcomes are poor risks and inadequate care, business people and organizations in investment activity, which ultimately leads to a decrease in their competitiveness. The risks are increased when growing uncertainty, instability, take place reform or undergoing rapid spontaneous changes in the economy. The phenomenon of growing uncertainty in the transformed economy not sufficiently researched. The main objective of the study was the development of the main directions of improving global competitiveness. The article presents the results of the analysis of competitive advantages and disadvantages of Russia in the world economy. Also examined the major factors contributing to or impeding to increased rating of the world competitiveness in the future. Based on this an attempt was made to develop basic strategic directions of increasing international competitiveness of Russia.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".