Public Innovation Policy and Other Determinants of Innovativeness in Poland
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
IntroductionInnovation is now regarded as a key factor of development of both businesses and economies. According to the neoclassical theory of economic growth, only technical progress is able to sustain long-term of economies in terms of per capita income (Solow, 1994). In developed countries, the share of Total Factor Productivity (TFP) reflecting technical progress in economic is about 60-80 per cent depending on the period for which the analysis is conducted (Hayami & Godo, 2005). The share of TFP in economic in Poland in the period 1999-2005 was 82 per cent (Siemek-Filus, 2008). The share of TFP in the of value added in industry and construction in Poland in the years 20022008 was 65 per cent(Wojnicka-Sycz, 2013). This means that Poland is already reflecting the path of development characteristic of developed countries and determined by factors such as innovation, human capital, and knowledge.The main weakness of the neoclassical model is that technological progress is outside the economic system - it is an exogenous variable, and thus the model does not include the possibility to influence technological progress. This drawback has been overcome by the so called theory of economic growth proposed by Romer and Lucas, in which a huge role in the of productivity is attributed to human capital, knowledge and learning by doing (Romer, 1990). Robert Lucas proved the right of rising revenues from knowledge at the level of society, but declining at the company level (Lucas, 2010). The new theory shows that technological progress and innovation can be effectively influenced, for example, by instruments of innovation and industrial policy.In Poland, since 2000, public innovation policy has become very important. It is executed at several levels: domestic, regional and, to a lesser extent, sectorial and local. Innovation policy in Poland is implemented via scientific, industrial and entrepreneurshippromoting policies as well as by means of regional policies carried out by regions themselves and on the domestic level by the ministry responsible for regional development. Moreover, some cities, especially metropolises, engage in pro-innovative activities like creation of science and technology parks. At all these levels most of the activities connected with innovation policy are co-financed by the European Union's structural funds. The European Union's structural funds support such activities as investment in modern technology and equipment in firms, acquiring patents, joint innovative projects between enterprises and scientific institutions, activities of business clusters or technology transfer centres, creation of laboratories for tenants of science and technology parks. The instruments of public innovation policy in Poland are thus varied and comprise innovation grants for firms, proinnovative institutions and universities for different purposes like investment and R&D staff's work, special loans, tax exemptions, creation of pro-innovative infrastructure like technology parks, preparation of regional and domestic innovation strategies, securing of intellectual property rights, promotion of knowledge networks, etc. Still, the amount of money available for support of innovativeness is low in comparison with the most developed countries and it is mainly channelled by means of policy connected with the European Union's support in the form of structural funds. Poland ranks on the European Innovation Scoreboard 2015 in the group of moderate innovators among some other former communistic and Mediterranean countries with results lower than the European Union's average. Efforts in innovativeness and R&D of these countries will depend on whether the European Union as a whole reaches the indicators of R&D&I of its main competitors like the USA or Japan, especially whether the share of R&D in Gross Domestic Product reaches the order of 3 per cent. …
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it