Bringing the Context Back In: Settings and the Search for Syndicate Partners in Venture Capital Investment Networks
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.008 | 0.011 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".