Risk Management in the System of Financial Stability of the Service Enterprise
Bibliographic record
Abstract
The article is devoted to the theoretical substantiation and development of methodological approaches and practical recommendations for modeling the assessment of the financial stability of a service sector enterprise. To assess the financial condition of the hotel industry, a visual interpretation of the neural network, a model of self-organizing Kohonen map, was used. It is proven that by the method of Kohonen maps for each service provided by the hotel industry, in a certain period of activity, it is possible to establish certain objective limitations of structural characteristics that will prevent the transition to problem clusters or ensure the transition to better ones. The authors propose an economic and mathematical model of the process of assessing financial stability by calculating the integral indicator of financial stability of the service sector. The types of control maps for each of the coefficients that have a significant impact on the assessment of the financial stability of the enterprise in the service sector were identified. Control maps were constructed for each coefficient, which are part of the integrated indicator of financial stability, and their analysis was carried out for the presence of special reasons for the variability of the process of financial stability assessment. The concept of modeling a system for assessing the financial stability of service enterprises is developed in the article, which is based on the collection of financial data, a comprehensive analysis of factors influencing the financial condition, a study of the controllability of the process of assessing financial stability, building a model of an integral indicator of financial stability, and its program implementation.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".