Is There Conceptual Convergence in Entrepreneurship Research? A Co–Citation Analysis of <i>Frontiers of Entrepreneurship Research</i> , 1981–2004
Bibliographic record
Abstract
Conceptual convergence is often seen as a holy grail in entrepreneurship research. Yet little empirical research has focused specifically on the extent and nature of this convergence. We address this issue by content–analyzing the networks of co–citation emerging from the 20,184 references listed in the 960 full–length articles published in the Frontiers of Entrepreneurship Research series between 1981 and 2004. Our results provide evidence for the varying levels of convergence that have characterized entrepreneurship research over the years, as well as the evolution of the conceptual themes that have attracted scholars’ attention in different periods. In addition, we provide evidence that the field relies increasingly on its own literature, something that points toward the unique contribution that it makes to the management sciences.
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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 | 0.015 | 0.090 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.047 | 0.081 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.001 | 0.004 |
| 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".