Anti-crisis Management of Socio-economic Systems Development in the Global Competitive Environment
Bibliographic record
Abstract
The article considers anti-crisis management of socio-economic systems in a global competitive environment. A combined methodological approach to strategic anti-crisis management of the socio-economic system of the state through the parameters of an open three-sector model. The criteria for minimizing the threat of outflow of significant resources to other socio-economic systems or reducing the inflow of new resources with a time limit on the level of risk, aimed at preventing and eliminating crises and enhancing long-term (strategic) management is determined. The conditions of quasi-crisis pressure. The method of hierarchical ordering of the dynamics of indicators for assessing the trajectory of the country's development from the standpoint of compliance with the strategy of anticrisis management in a competitive environment is presented. The priority factors of sensitivity of anti-crisis management of the socio-economic system are substantiated. The connection of incomes from the stage of development of the national economy is determined. The structure of the system of factors at different stages of the development of socio-economic systems of the national level is formed. The index of global competitiveness of the world is analyzed. The rings of the countries-leaders of the international competitiveness on components of the GCI index are defined. The scale of activity of transnational corporations (TNC) is analyzed. The conditions for changing the distribution of investment and labor resources, capital stock and capital investments between sectors of the socioeconomic system of Ukraine are analyzed. An optimization balance of resource allocation in the socio-economic system of Ukraine.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".