USE OF WORLD EXPERIENCE IN INCREASING INVESTMENT ATTRACTIVENESS OF UKRAINE REGIONS
Bibliographic record
Abstract
The article is devoted to the research of the issues of using the world experience of increasing investment attractiveness and its adaptation for the regions of Ukraine.The purpose of the study is to use world experience to increase the investment attractiveness of the regions of Ukraine.In scientific articles in the research methods of scientific cognition, comparison, generalization and comparison were used, which allowed to make effective conclusions on the basis of the conducted researches.The experience of formation and development of investment attractiveness of the territory of such European countries as Poland, France, England, Scotland, Ireland, Germany, Sweden is considered.The advantages of these countries in achieving positive results in the development of the investment sphere have been established.The experience of the USA and China is studied, as in terms of economic indicators these countries have a positive experience in forming the investment attractiveness of the territory.The results of generalization of positive world experience in promoting the investment attractiveness of the territory in tabular form are presented.The expediency of using certain elements of the mechanism of public-private partnership of Sweden, Canada, the USA at development of the mechanism of maintenance of investment attractiveness of regions of Ukraine is proved.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".