Bulletin of Chinese Academy of Sciences (Chinese Version)
Keywords
think tank, data-driven, discursive logic, triangulation, information chain
Document Type
Think Tank Research
Abstract
The rapid accumulation of data resources and the development of analysis technologies have expanded the scope of think tank research, and prompted think tanks to pay more attention to data intelligence. Meanwhile, higher requirements are put forward on the quality and innovation of the think tank. Facing the development needs of think tanks, i.e., modernization, innovation, and conscientization, this paper demonstrates the change of data-driven think tank researches from the perspective of the information chain. The paper analyzes the urgent need to reshape the discursive logic of the think tank, and discusses the construction scheme of triangulation for data-driven think tank research. Finally, several suggestions for optimizing the construction of think tanks are put forward, such as paying attention to the complementarity of correlation and causality, integrating the technical rationality and humanistic values, analyzing from the perspective of cyber-physical-human ternary space, seeking support from multidisciplinary knowledge, and constructing the engineering services of the think tank.
First page
153
Last Page
159
Language
Chinese
Publisher
Bulletin of Chinese Academy of Sciences
References
1 潘教峰. 智库研究的双螺旋结构. 中国科学院院刊, 2020, 35(7):907-916. Pan J F. Double helix structure of think tank research. Bulletin of Chinese Academy of Sciences, 2020, 35(7):907-916. (in Chinese) 2 Weaver R K. The changing world of think-tanks. PS:Political Science and Politics, 1989, 22(3):563-578. 3 马费成, 李志元. 中国当代情报学的起源及发展. 情报学报, 2021, 40(5):547-554. Ma F C, Li Z Y. The origin and development of information science in China. Journal of the China Society for Scientific and Technical Information, 2021, 40(5):547-554. (in Chinese) 4 潘教峰. 科技智库研究的DIIS理论方法. 中国科学报, 2017-01-09(07). Pan J F. DIIS Methodology of think tank of science and technology. China Science Daily, 2017-01-09(07). (in Chinese) 5 Clauset A, Larremore D B, Sinatra R. Data-driven predictions in the science of science. Science, 2017, 355:477-480. 6 欧阳剑, 周裕浩. 数据驱动型智库研究理念及建设路径. 智库理论与实践, 2021, 6(3):20-27. Ouyang J, Zhou Y H. Research on path of the construction of data-driven characteristic think tank in China. Think Tank:Theory & Practice, 2021, 6(3):20-27. (in Chinese) 7 勇美菁, 钟永恒, 刘佳, 等. 支撑兰德公司的智库数据体系建设研究. 情报理论与实践, 2019, 42(9):69-75. Yong M J, Zhong Y H, Liu J, et al. Research on the construction of think tank's data system of RAND corporation. Information Studies:Theory & Application, 2019, 42(9):69-75. (in Chinese) 8 Baker M. 1500 scientists lift the lid on reproducibility. Nature, 2016, 533:452-454. 9 Serra-Garcia M, Gneezy U. Nonreplicable publications are cited more than replicable ones. Science Advances, 2021, 7(21):eabd1705. 10 Miyakawa T. No raw data, no science:Another possible source of the reproducibility crisis. Molecular Brain, 2020, 13(1):24. 11 Malamatidou S. Corpus Triangulation:Combining Data and Methods in Corpus-based Translation Studies. London:Routledge, 2017.
Recommended Citation
SUN, Jianjun; PEI, Lei; MA, Yaxue; and LI, Yang
(2022)
"Thoughts on Data-driven Discursive Logic and Triangulation of Think Tank,"
Bulletin of Chinese Academy of Sciences (Chinese Version): Vol. 37
:
Iss.
2
, Article 2.
DOI: https://doi.org/10.16418/j.issn.1000-3045.20211130003
Available at:
https://bulletinofcas.researchcommons.org/journal/vol37/iss2/2
Included in
Databases and Information Systems Commons, Models and Methods Commons, Political Theory Commons