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| Frontier Influence of the Inter-Directorate in Project Planning: Case Studies of AI Field in NSF Data |
| Fan Lipeng1, Wang Yuefen1,2,3, Cen Yonghua2,3, Yang Jie1 |
1.School of Economics & Management, Nanjing University of Science & Technology, Nanjing 210094
2.Management School of Tianjin Normal University, Tianjin 300387
3.Institute for Big Data Science, Tianjin Normal University, Tianjin 300387 |
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Abstract The purpose of this study is to calculate and analyze the inter-directorate degree in project planning; it further identifies the front of project planning for exploring the inter-directorate influence and the frontier distribution of project funding while considering the balance of researchers taking part in programs with different directorates and how many directorates the program involves. First, we use the Rao-Stirling diversity indicator to calculate the inter-directorate degree of the programs before dividing them into high-crossing, medium-crossing, low-crossing, and non-crossing types. According to the funding trend and intensity of the program, we then construct the indicators of frontier identification and divide the programs into cutting-edge, hot-spot, potential, and decline types with different inter-directorate degrees. Finally, we analyze the inter-directorate influence on the distribution of frontier programs and elaborate more on the cutting-edge programs in high-crossing types. The research results show that, for National Science Foundation (NSF) data in the artificial intelligence (AI) field, the number of crossing programs was approximately equal to that of non-crossing programs while the average funding of cross-type programs was much higher than that of non-crossing programs. From the perspective of the distribution in frontier programs, the non-crossing programs were generally the potential type, while high-crossing and low-crossing types were more likely to be cutting-edge programs. The funding trend was higher for high-crossing type than that for other crossing types. Moreover, it was observed that the fronts of high-crossing programs tend to focus on neural and cognitive systems, natural and human systems, and information intelligence systems.
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Received: 22 July 2021
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