R&D and Innovation in Food Processing Firms in Transition Countries
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 it