Academic Collaboration in Entrepreneurship Research from 2009 to 2018: A Multilevel Collaboration Network Analysis
Bibliographic record
Abstract
Entrepreneurship research is widely regarded as an important basis for competitive advantage in a rapidly changing international business environment, enhancing capacities for sustainable business growth, economic activity, and the wealth of nations. In recent years, international cooperation has been considered to be one of the key factors promoting the sustainable development of entrepreneurial research. However, the evolution of the cooperative network of entrepreneurial research and the relationship between international cooperation and entrepreneurial research performance has not received the attention of most researchers. Therefore, we used a multilevel collaborative analysis method, i.e., country, city, institution and scholar, analyzing 2037 studies in this area from 2009 to 2018 from the Business Source Complete database by collaboration network analysis and bibliometric analysis. Our study tracked the evolution and cooperation trends in entrepreneurship research and detailed characteristics of international academic cooperation over the past decade, and we found the following: (1) The four types of cooperative networks have evolved over time, and generally conform to the distribution characteristics of the core periphery; cities, institutions, and researchers from central countries such as the United States, the United Kingdom, Canada, France and Germany occupy central positions in cooperation; they are scale-free networks and subject to the principle of priority connection. (2) The evolution of cooperative networks at different levels are non-conformal, there is a subtle relationship between micro-networks that can explain the distribution and changes in macro-networks. (3) International academic cooperation can promote the performance of entrepreneurial research, and cooperation has become the main theme of entrepreneurial research. These findings can help researchers to better study cooperative relationships in entrepreneurship research. Moreover, they can provide entrepreneurial decision support for national and local governments and contribute to the sustainable development of entrepreneurial research.
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 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.017 | 0.023 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".