On the “Anthropogenic” Foundations and Incentives for Economic Development
Bibliographic record
Abstract
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close attention of society, while social guidelines and indicators of the functioning of the economy remain unobvious, outdated, or even completely harmful, as, for example, the usual indicators of the SNA.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.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".