Nanotechnologies as an object of statistical monitoring: global and national practice
Bibliographic record
Abstract
The author justifies the need of making a critical assessment of the real condition of the development in the sphere of nanotechnologies by way of conducting a survey of the companies engaged in that sphere. The author generalized the international experience of conducting surveys of nanotechnology companies in the countries leading in nanotechnology production, namely USA, Canada, Germany, Finland, Australia, and Russian Federation, which also made it possible to select the problematic aspects characteristic for Ukraine's practice of statistical monitoring of the nanotechnology sphere. Special attention is given to covering the results of the survey of Ukraine's nanotechnology companies conducted in mid-2013. On the survey's results, a conclusion was made that Ukraine's nanomarket is now in its initial state of development, is fragmentary, unstable and geographically inhomogeneous. Besides, one can state a considerable Ukraine's lagging behind the world's leading countries in the development of the nanotechnology sphere. By the number of companies engaged in nanotechnology activities, our country is practically at the level of Canada, Germany and Finland in 2000-2005. However, unlike the global tendencies, where the main spheres of economic activities of the nanotechnology companies are the high tech sectors, the vast majority of Ukraine's nanocompanies belong, by type of economic activity, to high school units and other kind of institutions. The share of manufacturing companies using and/or creating nanotechnologies is very small and varies within 10-20% of all nanotechnology companies. On the whole, the level of engagement of Ukraine's companies in nanotechnology activities is very low, and vast majority of non-technological items are experimental. Another characteristic feature of Ukraine's nanotechnology market is a weak competitive environment and the companies' orientation to domestic consumer. There is almost no competition between the domestic companies. A certain struggle is only observed in the area of government funded R&D projects. At the same time, foreign companies are already beginning to seize certain market segments, while Ukrainian nanotechnology produce is practically undemanded on the foreign market. Besides, the author highlights the results of an investigation on the advisability of cooperation in the sphere of nanotechnologies with the countries of the Customs Union (first of all with the Russian Federation as one of the global leaders in funding the development of nanotechnologies) and EU countries. It also was proven that active development of nanotechnologies in Ukraine is hindered by a series of problems. The key problems are the lack of investment resources and skilled personnel, administrative barriers, low demand for the sector's produce etc. Besides, the author justifies the preconditions for reforming the system of statistical monitoring of nanotechnology activities in Ukraine.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 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".