Assessment of Adaptive Management of Economic Security of Enterprises in the Context of Globalization Challenges and Sustainable Development
Bibliographic record
Abstract
The relevance of the study is due to the aggravation of changes in external environment that affect economic security of business entities and require the increase in their economic security in the context of globalization challenges and sustainable development. The purpose of the study is to develop principles of assessing adaptive management of economic security of enterprises in the context of globalization challenges and sustainable development. The methodological basis of the research is the structural-functional approach, which makes it possible to consider economic security as a certain system, take into account adaptive behavior of enterprises considering the specifics of their activities, conduct a full and detailed analysis of adaptive management of economic security of enterprises, and ensure the segmentation of economic security by functional areas of its activity, as well as identifying critical security points of the enterprise's functioning. The authors suggested using taxonomic analysis to assess the adaptive management of the enterprises' economic security. The peculiarity of this proposed methodical approach is the establishment of the quality of the influence of analytical features on economic security based on the determination of the Euclidean distance between the standardized values of analytical features and the value of the reference vector and the calculation of the integral indicator of the assessment of the adaptive management of the enterprise' economic security. Approbation of the proposed approach was in relation to the functioning enterprise on the territory of Ukraine, Eurotrading LLC, which carries out production activities. The calculations and taxonomic analysis were based on the actual values of the analytical assessment of the financial, intellectual, personnel and production components of the enterprise's economic security for five years. The results of the application of the proposed methodological approach made it possible to determine the largest root mean square deviation of analytical features, including the duration of accounts payable turnover and the turnover ratio of fixed assets. This made it possible to propose directions of adaptive management to increase the enterprise' economic security in the context of globalization challenges and sustainable development.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".