The Importance of Networking to Entrepreneurship: Montreal's Artificial Intelligence Cluster and Its Born-global Firm Element AI
Bibliographic record
Abstract
While international business and economics literature once viewed companies as atomistic, disconnected units basing decisions solely on market incentives, recent studies have highlighted the importance of the so-called relational aspect or ‘network embeddedness’ (Andersen and Lorenzen 2007; Turkina, Van Assche, and Kali 2016). Businesses and business owners are embedded in a wide range of social relationships ranging from formal inter-organizational networks to informal networks such as friendships and family ties, all of which affect decision-making and business performance (Turkina, Van Assche, and Kali 2016; Turkina and Thi Thanh Thai 2013). Social networks stimulate business growth by reducing transaction costs, creating business opportunities, and generating knowledge spillovers. Conceptual and empirical research on the importance of networking to entrepreneurs is still rather limited, however. The purpose of this special issue is therefore to explore various aspects of network embeddedness and their implications for entrepreneurship and small business development.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".