Not All Bridging Ties Are Equal: Network Imprinting and Firm Growth in the Nashville Legal Industry, 1933–1978
Bibliographic record
Abstract
In this paper we focus on the temporal and historical conditions under which bridging ties from the past affect current organizational outcomes. Whereas previous research has shown that bridging ties have high decay rates and short-term effects, we explore the possibility that bridging ties may produce benefits over an extended period of time. In particular, we contrast the conventional view of bridging ties having rapidly decaying effects with two alternative network dynamics suggesting “accumulating” and “imprinting” effects. We propose that bridging ties have accumulating effects as a result of learning and redeployment of cumulated knowledge. We also predict that bridging ties exhibit an imprinted effect whereby the founding conditions surrounding the formation of some, but not all, ties yield long-lasting network benefits. We test our theory in the context of Nashville's legal industry, studying the formation and evolution of the professional network of lawyers' coemployment ties between 1933 and 1978. Consistent with our theory, we find that bridging ties produce network benefits over an extended period of time and trace back to the point of tie formation. Surprisingly, we also find that the imprinted effect is more robust than the rapidly decaying effect of bridging ties.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".