Entrepreneurship at a crossroads: <scp>Meta‐analysis</scp> as a foundation and path forward
Bibliographic record
Abstract
Research Summary This special issue on “Advancing Entrepreneurship Research through Meta‐analysis” was commissioned in the belief that many entrepreneurship research topics have reached a crossroads. As a maturing, dynamic, and growing field, researchers are generating ever more empirical evidence regarding the field's central questions. Researchers can continue down this road, but for many topics, it seems time to pause and take stock of what has been learned—a task meta‐analysis was created to accomplish. We describe how the special issue articles accumulate and clarify what is known about important questions. Two of the studies highlight that entrepreneurial organizations are in fact different from other organizational settings, and all lay foundations that open new avenues for inquiry. We conclude by summarizing the types of questions meta‐analysis can help answer going forward and the advanced meta‐analytic techniques that are becoming increasingly important for answering such questions. Managerial Summary This special issue was commissioned because many entrepreneurship research streams contain mixed evidence about the nature of important relationships. Such a situation makes it difficult for entrepreneurs to leverage academic findings as they make decisions and for researchers to understand what is known. Meta‐analysis is a set of statistical tools that allows for the reconciliation of evidence that points in different directions and thereby provides actionable guidance for entrepreneurs and a solid foundation for researchers to build on. This introduction summarizes the special issue articles and describes their contributions. One key overall implication that arises from this collection of studies is that much of what works in traditional organizations is likely to work quite differently in entrepreneurial contexts.
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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 | 0.271 | 0.555 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.013 |
| Bibliometrics | 0.015 | 0.022 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.016 | 0.013 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.008 | 0.015 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".