Entrepreneurship in Turkey and other Balkan countries: are there opportunities for mutual co-operation through internationalisation?
Bibliographic record
Abstract
Purpose The aim of this study is to evaluate the entrepreneurship activity in Turkey and the Balkan countries and to show in which fields they can cooperate in the future. Design/methodology/approach Document analysis was used in the research. In this context, taking into consideration the Global Entrepreneurship Index data published in 2019, the entrepreneurial potentials of Balkan countries, its current status was examined. Therefore, Turkey’s contribution to the development of entrepreneurial activities in the Balkan countries is shown in the study. Findings The results of the research show that entrepreneurship activities in the Balkan countries are not at the expected levels. In addition, it is determined that Turkey is in a central position in the Balkan’s entrepreneurship ecosystem in subjects such as especially, product innovation, risk capital, the ability of entrepreneurial start-up and its enterprises show high growth. Other Balkan countries may cooperate with Turkey about the production of technological products and technology transfer issues. Partner incubation programs can be formed. Training activities related to the entrepreneurship ecosystem can be organised together. Originality/value To the best of the authors’ knowledge, this paper is one of the first study that addresses the current situation of Balkan countries by analysing the entrepreneurship index scores of Turkey and Balkan countries (Albania, Bulgaria, Bosnia and Herzegovina, Montenegro, Romania, Croatia, Serbia, northern Macedonia, Greece and Slovenia). It also formulated suggestions on establishing cooperation with Turkey.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".