Explore the Optimal Node Degree of Interfirm Network for Efficient Knowledge Sharing
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
Background: Network structure is a critical issue for efficient interfirm knowledge sharing. The optimal node degree turns out to be decisive because it is generally regarded as a core proxy of network structural characteristics. This paper is to examine what is the optimal node degree for an efficient network structure. Methods: Based on an interaction rule combining the barter rule and the gift rule, we first describe and then build a knowledge diffusion process. Then using four factors, namely network size, network randomness, knowledge endowment of network, and knowledge stock of each firm, we examine the factors that influence the optimal node degree for efficient knowledge sharing. Results: The simulation results show that the optimal node degree can be determined along the change in outer factors. Furthermore, changing the network randomness and network size has little impact on node degree. Instead, knowledge endowment of network and knowledge stock of each firm both have significant impact on the node degree. Conclusion: We find that an optimal node degree can always be found in any condition, which confirms the existence of a balanced state. Thus, policymakers can determine the appropriate number of links to avoid redundancy and thus reduce cost in interfirm networks. We also examine how different factors influence the size of the optimal node degree, and as a result, policymakers can set an appropriate number of links under different situations.
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.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.001 |
| 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