Network-Independent Partner Selection and the Evolution of Innovation Networks
Bibliographic record
Abstract
Empirical research on strategic alliances has focused on the idea that partners are selected on the basis of social capital considerations. In this paper we emphasize instead the role of complementary knowledge stocks and knowledge dynamics, which have received surprisingly limited attention relative to social capital as forces behind the formation and dynamics of innovation networks. To marshal evidence in this regard, we design a simple model of partner selection in which firms ally for the purpose of learning and innovating, and in doing so create an industry network. We abstract completely from network-based structural and strategic motives for partner selection and focus instead on the idea that firms' knowledge bases must âfitâ for joint learning and innovation to be possible, and thus for an alliance to be feasible. The striking result is that, despite containing no social capital considerations, this simple model replicates the firm conduct, network structure, and contingent effects of network position on performance observed and discussed in the empirical literature.
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How this classification was reachedexpand
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 | 0.145 | 0.359 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".