Too Many Cooks Spoil the Broth? Geographic Concentration, Social Norms, and Knowledge Transfer
Bibliographic record
Abstract
Abstract A long tradition in social science research emphasizes the potential for knowledge to flow among firms colocated in dense areas. Scholars have suggested numerous modes for these flows, including the voluntary transfer of private knowledge from one firm to another. Why would the holder of valuable private knowledge willingly transfer it to a potential and closely proximate competitor? In this paper, we argue that geographic concentration has an effect on the expected compliance with norms governing the use of transferred knowledge. The increased expected compliance favors trust and initiates a process of reciprocal exchange. To test our theory, we use a scenario-based field experiment in gourmet cuisine, an industry in which property rights do not effectively protect knowledge and geographic concentration is common. Our results confirm our conjecture by showing that the expectation that a potential colocated firm will abide by norms mediates the relationship between geographic concentration and the willingness to transfer private knowledge.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".