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| Interdisciplinary Research Modes and Paths Mining in Scientific Research: From the Scholars' Perspective of Changing NSFC Code |
| Wu Xiaolan1, Guo Yujie1, Jiang Ning2 |
1.School of Journalism and Communication, Nanjing Normal University, Nanjing 210024
2.School of Business Administration, Anhui University of Finance and Economics, Bengbu 233030 |
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Abstract Owing to the requirements of scientific knowledge and social development, interdisciplinary research has become important in scientific research. During the NSFC project, the same scholar chooses different fund codes at different times, resulting in a co-occurrence relationship between different discipline application codes. This co-occurrence relationship proves, to some extent, the existence of interdisciplinary research behavior. Therefore, based on the code transformation perspective of NSFC, in this study, the interdisciplinary research modes and transfer paths have been studied. First, combining the hierarchical structure of NSFC discipline application codes, the diversity of individual research disciplines (DIRD) has optimized and then is applied to the interdisciplinary research mode and interdisciplinary occurrence path. Through research, it was found that the optimized measurement indicators can more accurately represent the diversity of individual research disciplines than the existing indicators. Moreover, a significant correlation exists between individual interdisciplinarity and whether the institutions belong to the “985 Project.” The number of scholars engaged in interdisciplinary research is also related to local economic level. In addition, there are typical scientific department transfer paths in project applications, and there exist obvious knowledge communities among 89 first-class disciplines. The results of this study not only further enrich the interdisciplinary measurement indicators of fund project data but also provide a useful reference for helping scholars to apply for interdisciplinary projects.
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Received: 12 April 2022
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1 汪志兵, 孙竹梅. 从SCI论文看NSFC对普通高校的资助效果[J]. 中国高校科技, 2016(10): 22-25.
2 陈仕吉, 康温和, 江文森. 跨学科研究在科学研究中越来越重要?[J]. 科学学研究, 2018, 36(7): 1153-1160, 1195.
3 张琳, 孙梦婷. 突破交叉科学研究同行评议困境[N]. 中国社会科学报, 2021-11-23(1).
4 Wu J, Jin M, Ding X H. Diversity of individual research disciplines in scientific funding[J]. Scientometrics, 2015, 103(2): 669-686.
5 吴江, 金妙. 基于基金代码共现的学科知识流动网络研究[J]. 情报杂志, 2016, 35(6): 23-28.
6 吴江, 金妙, 陈君. 基金视角下的学科知识流动网络构建与分析[J]. 图书情报工作, 2016, 60(8): 79-85.
7 Porter A L, Chubin D E. An indicator of cross-disciplinary research[J]. Scientometrics, 1985, 8(3): 161-176.
8 Shannon C E. A mathematical theory of communication[J]. Bell System Technical Journal, 1948, 27(3): 379-423.
9 Simpson E H. Measurement of diversity[J]. Nature, 1949, 163(4148): 688.
10 Herfindahl O C. Concentration in the US steel industry[D]. New York: Columbia University, 1950.
11 Brillouin L, Hellwarth R W. Science and information theory[J]. Physics Today, 1956, 9(12): 39-40.
12 Stirling A. A general framework for analysing diversity in science, technology and society[J]. Journal of the Royal Society, Interface, 2007, 4(15): 707-719.
13 Porter A L, Cohen A S, Roessner J D, et al. Measuring researcher interdisciplinarity[J]. Scientometrics, 2007, 72(1): 117-147.
14 Hill M O. Diversity and evenness: a unifying notation and its consequences[J]. Ecology, 1973, 54(2): 427-432.
15 Leinster T, Cobbold C A. Measuring diversity: the importance of species similarity[J]. Ecology, 2012, 93(3): 477-489.
16 Zhang L, Rousseau R, Gl?nzel W. Diversity of references as an indicator of the interdisciplinarity of journals: taking similarity between subject fields into account[J]. Journal of the Association for Information Science and Technology, 2016, 67(5): 1257-1265.
17 Leydesdorff L, Wagner C S, Bornmann L. Betweenness and diversity in journal citation networks as measures of interdisciplinarity—a tribute to Eugene Garfield[J]. Scientometrics, 2018, 114(2): 567-592.
18 Rafols I. Knowledge integration and diffusion: measures and mapping of diversity and coherence[M]// Measuring Scholarly Impact. Cham: Springer, 2014: 169-190.
19 Bordons M, Morillo F, Gómez I. Analysis of cross-disciplinary research through bibliometric tools[M]// Handbook of Quantitative Science and Technology Research. Dordrecht: Springer, 2006: 437-456.
20 赵星, 谭旻, 余小萍, 等. 我国文科领域知识扩散之引文网络探析[J]. 中国图书馆学报, 2012, 38(5): 59-67.
21 Zhu Y J, Yan E J. Dynamic subfield analysis of disciplines: an examination of the trading impact and knowledge diffusion patterns of computer science[J]. Scientometrics, 2015, 104(1): 335-359.
22 张琳, 刘冬东, 吕琦, 等. 论文学科交叉测度研究: 从全部引文到章节引文[J]. 情报学报, 2020, 39(5): 492-499.
23 刘丽帆, 张恒, 章成志. 基于学术文献引文内容的跨学科知识流动研究[J]. 情报理论与实践, 2022, 45(6): 24-31, 47.
24 Chakraborty N. Activities and reasons for using social networking sites by research scholars in NEHU: a study on Facebook and ResearchGate[C]// Proceedings of the 8th Convention Planner, Sikkim University, Gangtok, 2012: 395-402.
25 耿斌, 孙建军. 在线学术社交平台的用户行为研究——以ResearchGate平台南京大学用户为例[J]. 图书与情报, 2017(5): 47-53.
26 Oh J S, Jeng W. Groups in academic social networking services—an exploration of their potential as a platform for multi-disciplinary collaboration[C]// Proceedings of the 2011 IEEE Third International Conference on Privacy, Security, Risk and Trust, and 2011 IEEE Third International Conference on Social Computing. Piscataway: IEEE, 2011: 545-548.
27 Jiang J P, Ni C Q, He D Q, et al. Mendeley group as a new source of interdisciplinarity study: how do disciplines interact on mendeley?[C]// Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries. New York: ACM Press, 2013: 135-138.
28 Wu X L, Zhang C Z. Finding high-impact interdisciplinary users based on friend discipline distribution in academic social networking sites[J]. Scientometrics, 2019, 119(2): 1017-1035.
29 李长玲, 牌艳欣, 荣国阳, 等. 基于社交媒体弱关系的跨学科相关知识组合识别[J]. 情报理论与实践, 2022, 45(3): 125-132.
30 岳名亮, 马廷灿, 王桂芳, 等. 跨领域合作对科研产出的影响: 以国家自然科学基金资助的SCI论文为例[J]. 中国科学基金, 2016, 30(6): 551-555.
31 汪雪锋, 张娇, 李佳, 等. 跨学科团队与跨学科研究成果产 出——来自科学基金重大研究计划的实证[J]. 科研管理, 2018, 39(4): 157-165.
32 王卫军, 姚畅, 乔子越, 等. 基于词嵌入的国家自然科学基金学科交叉知识发现方法——以“人工智能”与“信息管理”为例[J]. 情报学报, 2021, 40(8): 831-845.
33 Wu J, Ding X H. Author name disambiguation in scientific collaboration and mobility cases[J]. Scientometrics, 2013, 96(3): 683-697.
34 Shao Z Y, Li Y M, Hui F, et al. Interdisciplinarity research based on NSFC-sponsored projects: a case study of mathematics in Chinese universities[J]. PLoS One, 2018, 13(7): e0201577.
35 Blondel V D, Guillaume J L, Lambiotte R, et al. Fast unfolding of communities in large networks[J]. Journal of Statistical Mechanics: Theory and Experiment, 2008, 2008(10): P10008.
36 Newman M E J, Girvan M. Finding and evaluating community structure in networks[J]. Physical Review E, 2004, 69(2): 026113. 责任编辑 潘尧 |
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