Assessment of Innovative Development of Regions in the Context of Structural Transformation of the Economy
Bibliographic record
Abstract
Today, the development of economic systems is taking place in an era of transformations that lead to the formation of new economic models. Russia is no exception here. The main impetus for the transformation processes in our country’s economy was the anti-Russian sanctions directly related to political events from the United States, Canada, European and other countries. All this poses threats to Russia’s political, economic and social security. Therefore, in modern realities caused by sanctions pressure, the key strategic task of overcoming crisis phenomena and shocks is the formation of a new industrialization based on domestic innovations. It is important to assess the innovative potential of the regions in order to further develop management decisions. The authors proposed and tested a methodological approach to assessing the level of innovative development of the subjects of the Russian Federation. The algorithm of this approach includes sampling official statistical data, determining localization coefficients, averaging and evaluating their dynamics. It was determined that in the period 2011-2022, less than a third of Russian regions improved their economic efficiency and innovation localization indicators. The leading regions of innovative development have a high level of localization of the studied characteristics. Regions characterized by low localization coefficients are traditionally lagging behind and have low indicators of economic, including innovative development. The problems of innovative development of regions under the influence of transformational processes are very multidimensional, therefore, in the limited space of this article, only an assessment of innovative development based on the author’s methodological approach is given.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 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".