On the “Anthropogenic” Foundations and Incentives for Economic Development
Notice bibliographique
Résumé
Purpose of research. The purpose of this article is to implement applied social indicators as the means of research, collection and processing of statistical and practical information. The article analyzes the guidelines and measures of the real effectiveness of state regulation and economic transformations that determine the prospects for implementing demographic policy on the example of the Krasnodar territory. The search for constructive solutions to the accumulated social problems of “saving the nation” is the subject of the most active and close attention of society, while social guidelines and indicators of the functioning of the economy remain unobvious, outdated, or even completely harmful, as, for example, the usual indicators of the SNA. For more than a quarter of a century, it has been known fr om a World Bank study conducted on the example of 192 countries of the modern world that 64% of economic growth is provided by human and social capital. It is also obvious that the quality of human resources is becoming the main factor for ensuring a competitive economy. Only with regard to demographic factors and structural characteristics of the demographic potential, the harmonization of the economic growth model with the solution of socio-demographic problems, it is possible to modernize the economy. Meanwhile, according to most experts, including the Institute of Socio-Economic Studies of Population of Russian Academy of Sciences, at least another ten years, population of Russia will decrease and in parallel, to worsen the situation with people's health. Materials and methods. Methodological conclusions and fundamental principles of modern economic science, including institutional economics, demography, social modernization, adaptation, and social market economy, are used instrumentally in this work. The information is based on analytical materials and official statistical data of institutions and departments of the Russian Federation, international economic organizations, expert assessments and periodical press data. The research is based on the socio-economic processes of the last decade in the Krasnodar territory, where frightening indicators of natural population growth have been recorded in recent years. Results. Generalizations and conclusions are important for the development of the economic program to overcome the socio-economic crisis, economic policy, and the choice of priority directions for the development of national and regional economy. The results obtained can serve as a further development of research on the use of an institutional approach to the analysis of economic transformations and problems of state regulation of the social sphere. Conclusions. The study analyzes the demographic state of the Krasnodar territory and lists the main weaknesses and reasons for the current demographic policy of the region. The study reveals the dependence of the consolidated budget of the region on the gross regional product and the average annual population, and provides recommendations for preserving and increasing the population of the Krasnodar territory.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,005 | 0,010 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,002 |
| Études des sciences et des technologies | 0,001 | 0,008 |
| Communication savante | 0,005 | 0,003 |
| Science ouverte | 0,001 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,011 | 0,001 |
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 source (Gemma direct ou Codex distillé), 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 ».