Technological Windows of Opportunity for Russian Arctic Regions: Modeling and Exploitation Prospects
Bibliographic record
Abstract
The problems of exploitation of technological windows of opportunity are of particular scientific and practical interest in terms of the development of Russia’s national economy, and the Arctic region, which has a strong mineral and raw materials potential, is important in terms of its use for achieving the technological and national security of the Russian state. Considering this, the study of the theoretical and methodical aspects of the development of emerging technological windows of opportunity is important and relevant for the regions of the Russian Arctic zone. The purpose of this study is to assess the potential and reserves for exploitation of the emerging technological windows of opportunity during the deployment of a new technological order by mobilizing material and human capital in the Arctic regions. Methodological tools for the study of this problem included dynamic series analysis, structural analysis, comparison, description, descriptive statistics, cross-correlation analysis, production function model and its visualization. An analytical review of scientific publications, a set of tools and methods of research, allowed to obtain the following scientific results: A significant variability in the contribution of science-intensive and high-tech industry to the formation of gross value added in the Arctic region has been revealed; meanwhile, we can note stable dynamics of the contribution of the Arctic economy to the gross domestic product (GRP) of Russia as a whole. There is a steady excess of the productivity index over the Russian average, which can be regarded as a potential for growth of high-tech components of labor in the development of the economy of the Arctic region. There is a negative statistically significant relationship between the share of the gross regional product of the Arctic in the Russian GRP and the share of gross value added (GVA) of science-intensive products in the Arctic GRP, which can be regarded as a factor preventing the exploitation of the emerging technological windows of opportunity. The construction of a model of production function of technological windows of opportunities for the Arctic zone of Russia pointed to the presence of potential in the exploitation of emerging technological windows in the Arctic zone of Russia in the development of human capital through the activation and use of high labor productivity, creating high-performance jobs. The results of the study, its findings and its proposals can be used in the development, monitoring and implementation of state federal and regional programs and projects aimed at improving the level of technology and science intensity of production in the Arctic zone, improving its competitiveness, which is highly important for the national economy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".