FORMATION OF INVESTMENT RISKS UNDER THE INFLUENCE OF THE COVID-19 PANDEMIC IN UKRAINE
Bibliographic record
Abstract
Purpose. The aim of the article is to study the situation and develop practical recommendations for improving approaches to investment risk management under the influence of adverse factors caused by the COVID-19 pandemic. Methodology of research. The following methods were used in the study: theoretical synthesis and observation (to identify investment risks and determine their possible consequences), comparison and analysis (to study the experience of countries in overcoming the situation caused by the coronavirus pandemic), tabular (to visualize risks and possible methods for overcoming them), generalization (for theoretical generalization and formulation of conclusions). The study is based on the hypothesis that the identification of investment risks will help to choose an effective investment policy of the state and attract the necessary investment resources needed for the development of our country and overcoming the crisis caused by the global coronavirus pandemic. Findings. Approaches to determining investment risks among domestic researchers are generalized. The impact of risks on the state of the economy in general and investment activities in particular is considered, and the possible consequences of economic, social, informational, political and technological risks on investment processes in the country are also determined. Possible options for dealing with the situation caused by the pandemic in various countries, including Italy (development of a financing program for manufacturers of medical devices), the United States (tax rebates for manufacturers related to the fight against COVID-19), Canada subsidies for training and development of digital skills of the population), Egypt (reduction of prices for natural gas and electricity for certain industries). Originality. Proposals were developed to overcome the consequences of the negative impact on the country’s economy caused by the COVID-19 coronavirus pandemic. Practical value. Substantiated on the results of the study, the proposals can be used by public authorities and business structures to take measures to prevent the negative consequences of investment risks and attract investment resources to overcome the economic crisis caused by the coronavirus pandemic. Key words: investment risks, investment policy, uncertainty, investment climate, COVID-19 pandemic, investment activity, investment security.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".