One Decade Research in the Field of Business Ecosystem: A Bibliometric Analysis
Why this work is in the frame
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Bibliographic record
Abstract
The business ecosystem is a new paradigm, highly popular among researchers and practitioners.Systematic literature reviews based on bibliometric analysis of business ecosystem studies are still difficult to find.This paper aims to conduct bibliometric and visualization analyses with VOSviewer in business ecosystems.The evaluation involved 44 scientific articles on business ecosystem studies indexed by Scopus quartile Q1 -Q4 from the Google Scholar database in the last decade, namely 2010-2020.Bibliometric analysis has found the most productive publishers, the development of scientific articles, and the number of citations.While visualization with VOSviewer has found the most common terms in titles and abstracts, author collaboration, and makes it easier for researchers to find new and rarely researched topics in the business ecosystem.
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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 | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.049 | 0.030 |
| 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