Search

This study used bibliometrics to analyze the characteristics of funded papers in the social sciences in Taiwan from 2015 to 2019, including the number of funded papers, funded paper ratio, document types, fields, type of publishing organizations, journal impact, and paper citation impact. The results indicate that the number of funded papers and the funded paper ratio of SSCI papers from Taiwan have both increased. In addition to articles and reviews, conference papers are a major type of funded paper for local SSCI papers. In terms of fields, the nontraditional social sciences, such as environmental sciences, psychiatry, and public/environmental & occupational health, have a particularly high funded paper ratio. However, the number of funded papers and funded paper ratio is the highest in the field of education & educational research. The fields of economics, management, and business have a large number of funded papers but a low funded paper ratio. Universities are the main funded institutions, but hospitals have the highest funded paper ratio. Funded papers tend to have higher paper citation impacts. Therefore, funding positively affects the productivity and impact of SSCI papers from Taiwan. (Article content in Chinese with English extended abstract)

Science funding plays a guiding role in the development direction of scientific innovation. As one of research funding providers, private companies influence the development of science and technology (S&T) through their selective support. Thus, strategy and layout of enterprises in S&T can be revealed by analysing their funded papers. Taking Google as the example, the paper proposes an analytical method of funded papers by the combination of co-word analysis, clusters analysis and social network analysis, so as to explore the scientific strategy and collaboration preference. The total 2162 valid bibliographic records of papers supported by Google from the Thomson Reuters Web of Science are divided into four groups according to discipline clusters using Hierarchical Clustering Algorithm. Social network analysis is conducted to detect communities among keywords and institutions. The results demonstrate that Google shows different funding patterns between traditional research fields and emerging industries. Famous universities are the main funding targets of Google, and the important institutions can be divided into two groups. [ABSTRACT FROM AUTHOR]