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Will the United States' Field-Weighted Citation Impact of AI research be at least 0.5 higher than China’s in 2019?


Field-Weighted Citation Impact (FWCI) is an indicator of the citation impact of a publication. It is calculated by comparing the number of citations actually received by a publication with the number of citations expected for a publication of the same document type, publication year, and subject. FWCI is always defined with reference to a global baseline of 1.0 and intrinsically accounts for differences in citation accrual over time, differences in citation rates for different document ages (e.g., older documents are expected to have accrued more citations than more recently published documents), document types (e.g., reviews typically attract more citations than research articles), and subjects (e.g., publications in medicine accrue citations more quickly than publications in mathematics).

Computer science research is disseminated in a variety of publication types (e.g., journals, conferences, etc.) and forms (e.g., software, code, etc.). Thus, while article citations may not fully capture research impact in the AI field, they nevertheless play a relevant role, especially for comparative benchmarking of entities on scholarly impact.

According to Elsevier's 2018 AI report (pg54) (also downloadable here) China’s AI research currently has a FWCI of 0.96, whilst the US has an impact of 1.49. The difference has historically been larger, with the US output typically just short of 1 FWCI point higher than China's.

Will US Field-Weighted Citation Impact of AI research remain at least 0.5 higher than China’s in Elsevier's 2019 report?


For this question, we will follow the methodology in Elsevier's 2018 AI report, which refers to refer to the data published by Scopus (Elsevier’s abstract and citation database of peer reviewed literature). This question resolves positive if the U.S. has a FWCI of its AI research output (across all document types) in 2019 that exceeds China's by 0.5 or more, and negative otherwise.


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