External Risk Factors Influence on the Financial Stability of Construction Companies
Bibliographic record
Abstract
The modern conditions of construction companies’ activities in Russia are influenced by various processes: developing globalization, limitation of free trade due to economic sanctions, man-made disasters growth, worldwide digitalization, constantly evolving technologies. The purpose of this study is to develop a model for assessing risk factors’ impact on the financial stability of construction companies using regression analysis based on dependencies between risk factors and financial stability of construction companies on the basis of statistical data over the past 10 years. The following methods were used: questioning of owners and key employees in construction companies on the indicators choice that characterize external risk factors, correlation analysis, regression analysis, expert evaluation method, trend line method. As a result it was revealed that in order to create favorable conditions for the construction companies’ growth, a stable legislative base, a stable ruble rate and an activation of investments in fixed assets are needed. The proposed tool for assessing external risk factors and their impact on the construction companies’ financial sustainability can be used both to assess the organization's environment and to assess various risk situations in order to further use the results in decision-making.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".