R&D in Poland: Is the Country Close to a Knowledge-Driven Economy?
Bibliographic record
Abstract
Abstract Poland has a strong ambition to evolve rapidly into a knowledge-driven economy. Since 2004, it has been the largest beneficiary of European Union cohesion policy funds among all member states. Between 2007 and 2013, Poland was allocated approximately EUR 67 billion, whereas for 2014-2020 the EU budget earmarked EUR 82.5 billion for Polish cohesion policy. This means that in the coming years, Poland’s R&D intensity will grow. But the question remains: is 27 years of free market economy enough to enable a country’s economy to become knowledge-based ? This paper offers an analysis of Polish R&D expenditures and investments in terms of their sources (business, government or higher education sectors), types (European Union or state aid) and areas of support (infrastructure, education or innovation). It also characterises the Polish R&D market with its strengths and weaknesses. Then, it examines the process of technology transfer in Poland, comparing it to best practice. Finally, the paper lays out the barriers to effective commercialisation that need to be overcome, and attempts to answer the question raised in its title.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".