Small and Medium-Sized Enterprises, Business Demography and European Socio-Economic Model: Does the Paradigm Really Converge?
Bibliographic record
Abstract
Although the European business environment induces important premises and assures conditions in determining economic growth and social well-being, the determinant and existent connections between the evolution of small and medium-sized enterprises (SMEs), business demography characteristics and the European socio-economic model have been scarcely studied in recent years. The dimensions of the European socio-economic model design a very specific framework in developing business demography and assuring a favorable environment for future SME development. The main aim of the manuscript is to investigate the evolution of the European SMEs sector and the perspective of business demography evolution to converge with exigencies of the European socio-economic model. In order to argue the research objective, eight specific and representative business demography variables were employed, from 12 European Union member states (EU-MS), during 2009–2017. Further, the SMEs’ performances, determined by changing the economic functional paradigm, were assessed. For proving this, an econometric model was designed considering labor productivity as an endogenous variable. Our preliminary analysis shows considerable differences in business demography indicators and SMEs development among all five socio-economic sub-models of the main European socio-economic model, proving a tight connection between European socio-economic models and SMEs’ performance and arguing the necessity of a paradigm convergence. Within some sub-models, there is clear evidence of clustering and convergence in terms of business demography and SMEs future development.
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.
How this classification was reachedexpand
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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".