Bringing the Context Back In: Settings and the Search for Syndicate Partners in Venture Capital Investment Networks
Why this work is in the frame
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Bibliographic record
Abstract
Most existing theories of relationship formation imply that actors form highly cohesive ties that aggregate into homogenous clusters, but actual networks also include many “distant” ties between parties that vary on one or more social dimensions. To explain the formation of distant ties, we propose a theory of relationship formation based on the characteristics of “settings,” or the places and times in which actors meet. We posit that organizations form relations with distant partners when they participate in two types of settings: unusually faddish ones and those with limited risks to participants. In an empirical analysis of our thesis in the formation of syndicate relations between U.S. venture capital firms from 1985 to 2007, we find that the probability that geographically and industry distant ties will form between venture capital firms increases with several attributes of the target-company investment setting: (1) the recent popularity of investing in the target firm's industry and home region, (2) the target company's maturity, (3) the size of the investment syndicate, and (4) the density of relationships among the other members of the syndicate.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it