Assessment of Regional Economic Security Level in Innovative Development
Bibliographic record
Abstract
Analysis of the innovative development of the regional economy is a relevant problem due to its role in ensuring the economic security of the country and achieving priority goals. The subject of the research are the regions of one of Russia's most dynamically developing macro-regions – the Volga Federal District. It features innovation clusters, a network of modern manufacturing companies, research organizations. At the same time, it developed a significant territorial heterogeneity of the regional space.Therefore, in the course of the study, a typology of the regions was drafted according to a number of indicators, which made it possible to assess the level of their innovative development and identify zones of relative stability, medium and critical state. A forecast of the main indicators of the innovation component was made showing the ability of the regions to overcome the factors preventing the development of the innovation economy.The research results showed that most of the regions have a medium level of economic security in the field of innovation. The Nizhny Novgorod Region and the Republic of Tatarstan are at a high level. The economy of these regions is characterized by a high level of diversification, resilience to instability in the domestic market and external challenges. The Saratov region, the Republic of Mari El and the Orenburg Region are in a low-level zone. A short-term forecast indicates that in general the situation will not change – the regions will increase or decrease the values of the indices within the achieved levels of economic security. A qualitative transition to a new level is possible provided that the problems that hinder the innovative economy in the regions are eliminated.
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 | 0.002 | 0.004 |
| 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) | 0.003 | 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".