R&D and Innovation in Food Processing Firms in Transition Countries
Bibliographic record
Abstract
Abstract We examine the implications of the liberalised economic conditions associated with the economic transformations in the transition countries of Central and Eastern Europe (CEE) and Commonwealth of Independent States (CIS) for R&D and innovation in the food processing sector. We use a dataset derived from the World Bank's Business Environment and Enterprise Performance Surveys (BEEPS) database to examine the relationships between R&D/innovation activities in food processing firms in transition countries and (i) privatisation, (ii) foreign direct investment, (iii) trade activities, (iv) market competition pressure, and (v) economies of scale. The empirical analysis is implemented through: (i) a double‐hurdle model for R&D participation and expenditures, and (ii) a bivariate probit model for product and process innovation. We find that these economic transformations generally promote R&D/innovation activities in the food processing sector. Our results suggest that broadened and deepened economic liberalisation policies would improve the innovation performance of the food processing sector in transition countries, and would enhance competitiveness in domestic and foreign markets. They also indicate that innovation policies may need to be tailored to market and industrial characteristics of different transition regions.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".