Modeling Study on Risk Identification in the Process of Anti-Crisis Enterprise Management
Bibliographic record
Abstract
The study provides solutions for the scientific task related to the improvement of theoretical and development of methodological and applied principles, and the identification and evaluation of risks and threats as factors of anti-crisis management of the enterprises. Based on the developed concept of quantitative risk analysis, we constructed a fuzzy hierarchical model, which gives the possibility to get the estimates: risk factors; specific types of threats in the framework of a process; risk processes, identified in the anti-crisis management; and the integrative risk of anti-crisis management. Furthermore, the proposed model makes it possible to identify the threats that are the risks of the highest (catastrophe) layer. The fuzzy hierarchical model construction process includes the determination of linguistic variables, term-varieties, and universal sets for quantitative evaluation of figures and risks, the establishment of parameters of the membership functions for indicators and risks, the formation of fuzzy knowledge bases, the construction of a fuzzy hierarchical model in the MATLAB environment, the evaluation of adequacy of model based on the learning sample, the correction of a model, and the adoption of a resolution regarding its final variant. The use of the model in the anti-crisis enterprise management will provide the anti-crisis team with the possibility to give early warning of all negative factors, give their quantitative evaluation, and take them into account in the course of making managerial decisions.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".