Sources of Financing High-Tech Projects
Bibliographic record
Abstract
This article is aimed at considering current sources of financing for high-tech projects. In the article, statistical data confirm the orientation of developed and developing countries' economies towards the generation of high-tech products by the dynamics of added value in high-tech and medium-high-tech industries, respectively - support for high-tech manufacturing enterprises and industries as a whole. Lists of high and medium-high-tech adopted sectors in the Russian Federation, in the United States, and in European nations, and a list of critical technologies are given. Special attention is given to the essence of the high-tech projects, concept, through the implementation of a rise in the share of high-tech products. The article identifies the main characteristics that distinguish a high-tech project from an innovative one. The dynamics of value-added in high-tech and medium-high-tech sectors of the economies of developing and developed countries are studied in conjunction with R & D spending in high-tech sectors of these countries. On the example of the United States (the global leader in the high-tech industry), the structure of financing high-tech projects carried out by companies at the expense of internal financing, that is, own funds is investigated. Based on the Russian Federation, the volumes of attracted cash in the form of grants and the amount of borrowed cash in the form of a subsidy for the implementation of high-tech projects in dynamics for 2012-2018 are investigated.
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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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".