Algorithm of Decision Making Process by Corporate Management and Ways of Resolving Crisis Situations Caused by Accounting, Financial and Economic Risks
Bibliographic record
Abstract
The scientific paper presents an algorithm and methods of strategic decision making process by top management and ways of eliminating and resolving crisis situations caused by accounting, financial and economic risks. The algorithm deals with a business's lifetime stages and presents the risks, as well as the methods to analyse such risks, based on both external and internal factors of managerial environment. The aim is to define the risks and ways of eliminating them. The paper includes results of the EP 7260 (Brno, 1998-2000), GA MSM 431100007 (Brno 2000-2001)s and EP - 12/2001-2003 (Brno, 2001-2002) research projects. Methodology is based on analytical-synthetic methods, comparison, controlled interview, strategic decision making process, crisis management methods and selected methods of the accounting, financial and economic analysis. The paper also follows up the works published at conferences and in scientific journals FŠI ŽU Žilina (2000), SPU FEM Nitra (2000-2002), PEF ČZU Praha (2000-2001) and IAES (Vienna, 1999), (Montreal, 1999), South Carolina (2000), and Paris(2002). Results of the research have been verified on selected enterprises in the process of dealing with crisis situations which afflicted these enterprises owing to unsuitable reactions to changes in the managerial environment.
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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".