Network Progeny? Prefounding Social Ties and the Success of New Entrants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Entrepreneurs that were employed by successful industry incumbents prior to founding tend to confer advantages on their new organizations. We propose and then demonstrate a similar “network progeny” effect rooted in the social relationships that form among entrepreneurs. Our analysis of new entrants into the Ontario wine industry shows that prefounding friendship ties of the founders of one especially prominent entrepreneurial firm led to significantly higher ice wine prices. This attests to the promise of a network progeny extension of the parent–progeny account of new firm success. Follow-on analysis indicates that this effect is not attributable to an entrant's ability to make ice wines of superior quality or to it having access to better distribution knowledge. We therefore conclude that having a social tie to this prominent entrepreneurial firm generated reflected prominence that enhanced the valuations and therefore prices of wines made by connected market entrants. This paper was accepted by Jesper Sørensen, organizations.
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 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