Technological Windows of Opportunity for Russian Arctic Regions: Modeling and Exploitation Prospects
Notice bibliographique
Résumé
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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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».