G2TT
来源类型Report
规范类型报告
DOIhttps://doi.org/10.7249/RRA1482-3
来源IDRR-A1482-3
An Open-Source Method for Assessing National Scientific and Technological Standing: With Applications to Artificial Intelligence and Machine Learning
Jon Schmid
发表日期2021-10-28
出版年2021
页码20
语种英语
结论 An Open-Source Method for Assessing National Scientific and Technological Standing: With Applications to Artificial Intelligence and Machine Learning | RAND
摘要

The author of this report develops a quick-turn and open-source methodology for assessing national standing in science and technology (S&T) for a given field. The approach entails the calculation of four metrics: high-impact publications, collaborative network density, quality-adjusted patents, and S&T organizational capacity for an analyst-defined S&T area. Following its presentation, the methodology is applied to the field of artificial intelligence and machine learning for nine countries: Germany, France, the United Kingdom, South Korea, Japan, India, Russia, China, and the United States. Using this approach, the author finds that the United States ranks first in three metrics (high-impact publications, collaborative network density, and S&T organizational capacity) and second to China in quality-adjusted patents. The author concludes the report by using the data collected to implement the methodology to explore three additional topics: international patterns of collaboration, the role and research foci of particular organizations, and an application area: the intersection of artificial intelligence and cybersecurity technology.

目录 An Open-Source Method for Assessing National Scientific and Technological Standing: With Applications to Artificial Intelligence and Machine Learning | RAND
主题Bibliometrics ; Emerging Technologies ; Machine Learning
URLhttps://www.rand.org/pubs/research_reports/RRA1482-3.html
来源智库RAND Corporation (United States)
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条目标识符http://119.78.100.153/handle/2XGU8XDN/524609
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Jon Schmid. An Open-Source Method for Assessing National Scientific and Technological Standing: With Applications to Artificial Intelligence and Machine Learning. 2021.
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