Main development trends in public support of business research and development in Czechia
Bibliographic record
Abstract
Abstract The article aims at identifying main structural characteristics and development trends of business R&D support in the Czech Republic in the period 2007–2015 and their assessment in light of the development of total R&D expenditures in the business sector. Possible impacts of business research support on R&D expenditure from own resources of businesses and on R&D employment are also examined at the level of individual economic sectors. The volume of support annually allocated from the state budget to non-investment R&D activities of businesses culminated between 2009 and 2012. Compared to domestic companies, the companies in foreign ownership obtained only a quarter of public support but their share in total R&D expenditures in the business sector exceeds over a long period the share of domestic companies. Moreover, the difference has further grown in the last years mainly due to rapid increase of private funding from abroad. As regards to the size of businesses the reduction of public funding in the last years occurred primarily in the group of large businesses. However, the decreasing volume of public funding impacted only minimally on large businesses while the reliance of medium and especially small businesses on public resources is considerably higher. Substantial amounts of R&D support from the state budget were allocated particularly to high-tech and medium high-tech industries: manufacture of computer, electronic and optical products; manufacture of machinery; and manufacture of other transport equipment. Unlike in the majority of industrial branches where the absolute annual amounts of public support decreased during the last years, the amounts allocated to the sector of IT services grew significantly. Neither increase of R&D expenditure from own resources of businesses nor increase of R&D employment indicate dependence on the share of domestic public resources in BERD at the level of NACE branches.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".