Bibliographic record
Abstract
Abstract Much research suggests that social networks shape the emergence and development of nascent ventures. Scholars have argued that founders' and firms' networks influence innovation and the identification of entrepreneurial opportunities, as well as facilitate the mobilization of resources for growth and the harvesting of value from fledgling firms. It is not an exaggeration to claim that existing empirical findings point to the centrality of networks in every aspect of the entrepreneurial process. However, with exceptions so few they may be counted on one hand, this research untenably treats network structures as exogenous—in other words, as if entrepreneurs and enterprises do not pursue valuable connections. In this article, we review the literature on networks in entrepreneurial contexts, argue that it disproportionately focuses on the consequences of networks at the expense of research on their origins, and consider the implications for the literature of the fact that most entrepreneurs and young ventures are strategic in their formation of relations. We then articulate a research agenda composed of five areas of inquiry we consider critical to a better understanding of networks and entrepreneurship. Copyright © 2008 Strategic Management Society.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".