Financial Crisis Management of Business in Eastern Europe in the Context of the Resilience Increase of National Economic Systems
Bibliographic record
Abstract
Motivation: The research has noted great problems of post-Soviet Eastern European countries related to resilience securing of national economic systems, where the defining role belongs to financial crises at the micro-level caused by problems in the development and implementation of financial crisis management. The objective of this research is to study the problems of ensuring the operational efficiency of economic entities in Eastern Europe and to develop a set of measures to increase this efficiency.Novelty: The scientific novelty of the article is a developed set of ways to increase the performance of financial crisis management at the enterprises of post-Soviet Eastern European countries.Methodology and Methods: The methods of data study used in this research represent the quantitative analysis of statistic data. The research has also used the forecasting method of time series, which has suggested using regression functions to forecast future values based on data observed earlier.Data and Empirical Analysis: To conduct the research, data on four Eastern European countries, which were a part of the Soviet Union, for 2008-2018 have been taken. Policy Considerations: Post-Soviet Eastern European countries within the resilience securing of their national economic systems require the development and implementation of ways to increase the performance of financial crisis management at enterprises.
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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 | 0.002 |
| 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 | 0.004 | 0.002 |
| Open science | 0.000 | 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".