Bibliographic record
Abstract
Interfirm networks often take on characteristics consistent with the notion of a small world—they are locally clustered into dense sub‐networks or cliques that are sparsely connected by a small number of ties that cut across the cliques, linking network members through a relatively small number of intermediaries. Are these characteristics an emergent property of interfirm networks that result from chance connections among firms, or more strategic partnering by firms to improve or protect their network positions? After outlining a behavioral account for this frequently observed network topology, we show that the evolving investment bank syndicate network in Canada exhibited small world properties from 1952 to 1990. We then identify the investment bank cliques comprising the network and the ‘spanning’ ties that cut across them, and test three distinct scenarios that may explain the formation of these ties, which are responsible for the small worldliness of the network: (i) chance partnering of firms in different cliques; (ii) insurgent partnering by peripheral firms to destabilize the network and improve their network positions; and (iii) control partnering by core firms to maintain the network status quo and their positions within it. All three scenarios played a role in explaining the formation of clique‐spanning ties; however, chance and insurgent partnering played a greater role in our empirical setting. Our analysis of how small world structures emerge and evolve over time offers new insight into the origins of a prevalent interfirm network topology, and a baseline for constructing future models of interfirm network evolution and dynamics.
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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.028 |
| Scholarly communication | 0.014 | 0.043 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.018 | 0.002 |
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".