Methodological Tools for Investment Risk Assessment for the Companies of Real Economy Sector
Why this work is in the frame
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Bibliographic record
Abstract
Methodological approaches to investing in companies and reducing the negative impact of risks that are formed at the macro and micro levels are considered in the article. The algorithm for expressing investment risks through related risks and conducting an investment risk assessment as a group process is defined. It has been determined that the defining features of investment risks are the environment, duration, and scope of the project, risk position, profile, risk appetite, consequences, capacity, and results of the impact on the investment project. An investment risk accounting system is formed, which is represented by a set of organized structural elements that perform functions related to planning and implementation of a set of measures that identify, assess, monitor, and control risks to minimize negative consequences and enhance opportunities. A method of forming a real portfolio of investment projects considering the dynamic risk factor has been developed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it