Trends in and contributions to entrepreneurship research: a broad review of literature from 1996 to June 2012
Bibliographic record
Abstract
This article, which began as an effort to gauge trends in and contributions to the broad field of "entrepreneur/entrepreneurship," reviews 5,476 academic articles on entrepreneurship that were published in 522 Social Sciences Citation Index and Science Citation Index journals from 1996 to June 2012. This survey identifies keywords and conducts a review to search for and identify related articles in the Institute for Scientific Information Web of Science database. We then present our findings, including the number of publications by year, categorization of article types, main academic journals, authors, and most-cited articles. The citation counts for authors, journals, and articles are also analyzed. This study indicates that the number of articles related to the keyword entrepreneur increased from 1996 to the end of 2011, which is a sign of an upward trend in the influence of entrepreneurs. Entrepreneur research fascinated numerous scholars during the study period covering 16.5 years. In particular, researchers from the USA, England, Canada, Germany, and the Netherlands have made the most contributions to this field. This literature review provides evidence that the concept of entrepreneur attracted academic researchers, resulting in significant contributions to the field of entrepreneur research.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.027 | 0.036 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".