The Innovative Model for the Formation of a Database Used to Assess a System of Economic Security of Retail Companies
Bibliographic record
Abstract
The article highlights topicality of an issue related to assessing economic security of retail companies, which face hazards and threats under contemporary unstable economic conditions, for making decisions concerning ensuring a high level of the economic security, efficiency, and sustainable development of a company in general. The authors have developed a model of forming a database for assessing economic security of retail companies using mathematical modelling in order to avoid difficulties in the process of forming the database. Application of mathematical methods enables to create the more informative database, which will be used to conduct a more thorough analysis. This allows to make effective managerial decisions regarding ensuring a high level of economic security of a retail company. A methodical tool of M. Pohozhykh and M. Safronova underlies the model of forming the database for assessing economic security of a retail company applying methods of mathematical modelling. The methodical tool consists in modelling an n-dimensional geometric shape, namely a n- dimensional parallelepiped, taking into account properties of the Euclidean space. The model of forming the database for conducting an assessment is an outcome of the scientific research.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".