Multiplicative Methodology for Assessing Investment Attractiveness and Risk for Industries
Bibliographic record
Abstract
Creating favorable conditions for the development of industry is one of the key tasks with an increased level of complexity, the solution of which is associated with attracting investments and forming an investment policy that takes into account various specific characteristics of its implementation. However, modern science requires a deeper development of tools related to the study of the investment attractiveness of industries and the level of risk of investing in them, including taking into account market value factors. The purpose of this study is the development and practical approbation of a multiplicative methodology for assessing investment attractiveness and risk for individual industries. The methodological basis of the study was the scientific works of domestic and foreign scientists in the field of industrial and investment policy, its goals, tools for implementation and features of formation in individual industry complexes. The work also used the methods of structural-functional, economic-statistical, and comparative analysis, as well as tabular and graphical interpretation of empirical-factual information. The proposed methods for assessing investment attractiveness make it possible to take into account not only quantitatively measured indicators, but also more obscure indicators, which is especially important for obtaining a more complete result and can be used in conditions of limited access to information. As a result of this study, the most investment-attractive enterprises and a separate industry were identified, which at the initial stage should become the priorities of the industrial policy of the regions, since they are a kind of growth pole that can create a propulsive effect for the development of other enterprises and the territory as a whole.
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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 | 0.011 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.011 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| 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".