Government subsidy design: knowledge transfer in R&D networks considering risk attitudes and reputation effects
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
The design of government subsidies is essential in supporting collaborative innovation and promoting sustainable development in R&D networks. This study explores the influence of different government subsidy strategies designed for R&D networks on inter-enterprise knowledge transfer. Drawing upon evolutionary game theory, it examines how impact is contingent upon enterprises' risk attitudes and reputation effects. The results indicate that when enterprises exhibit homogeneous risk attitudes, government subsidy policies encouraging risk-seeking behaviors can effectively enhance the knowledge-transferring level. When enterprises possess heterogeneous risk attitudes, a greater diversity of risk attitudes leads to a more conducive environment for knowledge transfer. Incorporating a rigorous reputation tolerance into the design of government subsidies in R&D networks can effectively elevate knowledge transfer. Therefore, policymakers can design tailored government subsidies and incentive mechanisms grounded in enterprises' risk attitudes and reputation effects. This study provides theoretical and policy implications for designing government subsidies and collaboration in R&D networks.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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