Benchmarking the Turkish Business Incubators: Supporting Innovation through Innovative Infrastructures
Bibliographic record
Abstract
As business incubators (Bl) are one of the infrastructures used to promote and support entrepreneurs throughout the world in order to support the realization of innovative ideas, this paper will present a benchmark study of the Turkish business incubators to understand the existing infrastructure and to develop policy suggestions for the improvement of this infrastructure. Even though the main focus of the paper will be the Turkish Bl, the comparison will be based on two international studies. One of them is an international study of 12 selected countries (Armenia, Canada, Croatia, Hungary, Norway, Poland, Ukraine, Republic of Moldova, Republic of Serbia, Romania, Slovakia and Slovenia). The second study is carried out in 269 Bl across European member countries and around 2000 companies. Seven key factors seem to be used as indicators for benchmarking Bis: 1) the role of stakeholders, 2) locational and physical aspects of incubator operations, 3) the definition of the incubator's 'mission', 4) the tenants companies they attract as clients, 5) issues relating to the financing of incubator start up and operating cost, 6) governance, and 7) sustainability of Bl. The empirical part introduces the Turkish case by presenting the survey results of 11 business incubators, representing 27% of business incubators operating in Turkey. Based on the benchmarking analysis, three main results can be driven for policy makers. First, Turkish Bis have higher share of partnership structure as well as private Bis but business involvement seems restricted to being a formal partner, except for the business-owned Bis. Second, firms in Bis focus in manufacturing related technologies, demanding more involvement of universities in order to change firms orientation into technology. Third, Turkish Bis need to establish more professional management. Privately-owned Bis in Turkey employ professionals, while government-owned Bis in Turkey are run with appointed managers.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".