GLOBAL MARKET OF INTELLIGENT INVESTMENTS: EXPERIENCE FOR UKRAINE
Bibliographic record
Abstract
The article deals with the meaning of the concept of «intellectual investment». It was found that this economic category is quite diverse and most foreign and domestic scientists give only a general definition. Based on the studied approaches to understanding the essence of intellectual investment, the authors provide their own interpretation of this economic category. So, intellectual investment is any investment in intangible assets: training and retraining, research and development, transfer of know-how, creation of innovative products for additional economic benefits. A number of features that distinguish investments in intellectual capital from other types of investments are identified and attention is paid to approaches to the classification of types of intellectual investments. It is established that the leading countries in the implementation of intellectual investment in 2020 are China, the United States, Japan, Britain, Germany, and the trend of increasing the share of spending on innovation is observed in such regions as Asia and the Middle East, respectively. The place of the countries in the ranking of the Global Innovation Index, which is headed by Switzerland, Sweden and the United States, followed by Great Britain and the Netherlands, is described. The level of development of intellectual investments in Ukraine in terms of financing of innovation activities during 2016-2020, as well as in terms of receipt of applications for industrial property in Ukraine and the world is analyzed. The main negative factors that hinder the development of intellectual investment in Ukraine are assessed, and on the basis of world experience the effects that can be obtained as a result of investing in intellectual capital at different economic levels are highlighted. Due to the fact that Ukraine is losing its authority and attractiveness in the field of invention in the international arena, it is proposed to create a clear program to attract investment in intellectual capital, increase the share of budget funds for development and implementation of innovations, introduce programs to encourage the return of scientists. who previously emigrated.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".