Venture capital as a method of financing enterprise development in Central and Eastern Europe
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
Purpose The paper aims to increase the understanding of the venture capital industry in Central and Eastern Europe (CEE) by examining the venture capital industry in Hungary, Poland, the Czech Republic, and Slovakia between 1998 and 2003. Even though the number of academic studies focusing on the venture capital activities in the CEE region has been increasing in recent years, the coverage of this industry is relatively weak and not well understood by individuals, businesses, and academics. Design/methodology/approach The study focuses on the analysis of secondary data available from the European Venture Capital Association on venture capital activities in the CEE region. The paper examines three key statistics that best describe the venture capital process, namely fundraising, investing, and exiting activities. Findings The study has three conclusions. First, venture capital financing continues to be a major source of capital to the developing firms in the region. Second, Poland is the market leader in the region in the venture capital activities as described by key statistics. Third, the countries of CEE cannot be treated as a homogeneous block. Originality/value The study is important for two reasons. First, the study focuses on longitudinal data between 1998 and 2003, the most important period in the development of the industry. Shifts in trends in these key statistics can only be observed by analyzing longer‐term data series. Second, the evolution of the venture capital industry in the analyzed countries could be used as a blueprint for venture capital development in other countries in the region.
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 | 0.000 | 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