An integrated analysis of enterprise economy security
Bibliographic record
Abstract
With the complication of the business environment of commercial economic activity, competition intensifies, which threatens the bankruptcy of enterprises, the prevention of which requires quality monitoring and timely identification of crises using methods of comprehensive assessment and analysis of economic security. This research is aimed at conducting component-by-component and, as a result, integrated assessment of the state of economic security of the enterprise. Methodological research tools include analysis of the main components, causation and vector regression modeling. A resource-functional security model is developed (which consists of partial indicators and components of economic security of business) and a resource-functional approach to calculations is also applied. Using the data of the expert survey, the values of indicators of structural components of economic security of the enterprise are determined. Using the resource-functional approach, the integrated values of sub-indices and the integral values of the general level of economic security of the enterprise are calculated. According to the results of the assessment, it is established that the integrated level of economic security of the enterprise is 7.04 (sufficient level of security). However, the components of economic security identified critically low values, namely - the financial component (0.452), the information component (0.554), the institutional and legal component (0.647). The results of the study are of practical value for the development of technological schemes - algorithms for strengthening the financial, informational and institutional and legal security of the enterprise, making sound (using economic and mathematical tools) management decisions to ensure the trajectory of sustainable economic development.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".