The Transformation of Economic Development in the Border Regions of Russia and Kazakhstan in the Post-Soviet Period
Bibliographic record
Abstract
The prospects and trends for the development of border regions of the former Soviet Union have become one of the profound research areas in the field of economic geography recently. In the conditions of planned economy in the Republics of the USSR, a vertical system of industrial complexes was formed, with the focus on performing national economic tasks. There have been some significant changes in the border regions of independent post-Soviet States in the process of transition to the market economy model. The analysis of the industrial and territorial structure is done on the example of Russia and Kazakhstan. The formation of a common market on the basis of the Eurasian Economic Union allowed the border regions to make the most of their competitive advantage in attracting investments. The unique geographical particularity of the research object is manifested in the fact that there are no analogues of the longest land border in the world as between Russia and Kazakhstan. The new forms of production organisation are implemented in the border regions of the studied countries over more than 7,000 km. More than a quarter of a century later, transformation processes are clearly observed in the mining and manufacturing industries, agriculture, transport and services. As a result, the “regional asymmetry” of industrial development can be observed when manufacturing regions with high added value become the “cores” of economic development of cross-border relations between Russia and Kazakhstan.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".