DEVELOPMENT OF THE RUSSIAN FAR EAST AS A LOCOMOTIVE FOR THE ECONOMIC GROWTH OF THE COUNTRY
Bibliographic record
Abstract
The article is devoted to the issues of economic development of the Far Eastern territories of Russia. The Russian Far East occupies a rather advantageous economic and geographical position in Russia and the Asia-Pacific region since it borders with China, Japan and the United States. The purpose of the research is to determine the main directions for improving the economic development of the Far Eastern coastal territories, to compare the development of the coastal territories of the Far East, namely, the territory of Primorsky Krai with other countries, to provide a comparative analysis and draw certain conclusions. The area of the region makes up 36 % of the whole Russian territory while its population is only 4 % of the country’s population, which is due to severe weather conditions and weak economic development. The gross regional product to the Far East accounts for 5.7 % of the GRP of Russia. The paper features foreign experience in the development of coastal territories in the USA, China, Japan and Canada. It can be concluded that Russia significantly lags behind these countries in terms of GDP, exports, involvement in trade with the Asia-Pacific Region, and the sale of manufacturing products. A comparative analysis showed that the industrial structure of Russian coastal areas is similar only with those of Canada in raw material orientation of shipped goods, population of the port cities, whereas the indicators of the volume of Far Eastern cargo transportation in Russia lag behind all the countries examined. Apparently the main areas of development of the Far Eastern territories are: structural changes in the economy; attracting foreign investment in the creation of both mining, logging, and processing industries; development of transport infrastructure.
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.
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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".