基于创新扩散理论的学术论文影响力广度研究
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摘要
[目的/意义]采用被引次数衡量学术论文影响力存在诸多弊端。本文认为学术论文影响力包括其传播的深度、速度和广度3个方面,熵可用于衡量学术论文影响力传播的广度。[方法/过程]选择1901-2017年生物医学领域的诺贝尔奖论文作为最具影响力的论文纳入实验组,并根据1:1配对原则设立对照组,比较两组论文发表后5年内其施引文献所属学科数量、熵以及熵与被引次数的相关性。[结果/结论]实验组中65%以上的论文施引文献学科数量高于对照组;实验组熵的均值介于0.552-0.772,对照组介于0.251-0.481,有显著性差异(P<0.05);两组论文的被引次数与熵的相关性较弱,均小于0.3。结果表明:①70%以上的高影响力论文在发表早期能够影响较多的学科;②采用熵对论文影响力广度进行识别具有可行性。
[Purpose/significance] Using citations as a measurement of the impact of an article have long been criticized. This study argues that the impact of scientific paper includes its depth, speed, and breadth in diffusion. We can measure the impact breadth of an article by using entropy. [Method/process] This study regards Nobel Prizes winning articles in Physiology and Medicine field as the most influential scientific research, collecting Nobel Prizes winning articles into the test group and matching them at a ratio of 1:1 into the control group. The citing articles' disciplinary diversity, within five years after these papers' publishing, was explored. In addition, this study employs entropy to measure the diversity. [Result/conclusion] 65 percent of articles in the test group have relatively higher disciplinary diversity compared to the control group. The values of entropy in the test group are between 0.552 and 0.772, and between 0.251 and 0.481 in the control group(p<0.05). There is a weak correlation, which below 0.3, between citation counts and values of entropy. The paper argues that above 70 percent of highly influential articles have a high disciplinary diversity in their early stage, and it is possible by using entropy as an indicator to measure the breadth of an article's impact.
[Purpose/significance] Using citations as a measurement of the impact of an article have long been criticized. This study argues that the impact of scientific paper includes its depth, speed, and breadth in diffusion. We can measure the impact breadth of an article by using entropy. [Method/process] This study regards Nobel Prizes winning articles in Physiology and Medicine field as the most influential scientific research, collecting Nobel Prizes winning articles into the test group and matching them at a ratio of 1:1 into the control group. The citing articles' disciplinary diversity, within five years after these papers' publishing, was explored. In addition, this study employs entropy to measure the diversity. [Result/conclusion] 65 percent of articles in the test group have relatively higher disciplinary diversity compared to the control group. The values of entropy in the test group are between 0.552 and 0.772, and between 0.251 and 0.481 in the control group(p<0.05). There is a weak correlation, which below 0.3, between citation counts and values of entropy. The paper argues that above 70 percent of highly influential articles have a high disciplinary diversity in their early stage, and it is possible by using entropy as an indicator to measure the breadth of an article's impact.
引文
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[28] EGGHE L. Theory and practise of the g-index [J]. Scientometrics, 2013, 69(1): 131-152.
[29] GARFIELD E. The history and meaning of the journal impact factor [J]. Journal of the American Medical Association, 2006, 295(1): 90-93.
[30] BERGSTROM C. Eigenfactor: measuring the value and prestige of scholarly journals [J]. College & research libraries news, 2007, 68(5): 314-316.
[31] 任胜利. 基于引证网络分析期刊和论文的重要性 [J]. 中国科技期刊研究, 2009, 20(3): 415-418.
[32] FOSTER J G, RZHETSKY A, EVANS J A. Tradition and innovation in scientists’ research strategies [J]. American sociological review, 2015, 80(5): 875-908.
[33] WANG J, VEUGELERS R, STEPHAN P. Bias against novelty in science: a cautionary tale for users of bibliometric indicators [J]. Research policy, 2017, 46(8): 1416-1436.
[2] ZHANG L, PENG T-Q. Breadth, depth, and speed: diffusion of advertising messages on microblogging sites [J]. Internet research, 2015, 25(3): 453-470.
[3] 伊斯利,克莱因伯格. 网络、群体与市场 [M]. 北京:清华大学出版社, 2011.
[4] JONES A Z. What is velocity in physics? [EB/OL]. [2018-10-08]. https://www.thoughtco.com/velocity-definition-in-physics-2699021.
[5] WANG D, SONG C, BARABASI A L. Quantifying long-term scientific impact [J]. Science, 2013, 342(6154): 127-132.
[6] UZZI B, MUKHERJEE S, STRINGER M, et al. A typical combinations and scientific impact [J]. Science, 2013, 342(6157): 468-472.
[7] GARFIELD E. Citation indexes for science: a new dimension in documentation through association of ideas [J]. Science, 1955, 122(3159): 108-111.
[8] MIN C, SUN J, DING Y. Quantifying the evolution of citation cascades [J]. Proceedings of the Association for Information Science and Technology, 2017, 54(1): 761-763.
[9] MIN C, DING Y, LI J, et al. Innovation or imitation: the diffusion of citations [J]. Journal of the Association for Information Science and Technology, 2018, 69(10):1271-1282.
[10] ZHAI Y, DING Y, WANG F. Measuring the diffusion of an innovation: a citation analysis [J]. Journal of the Association for Information Science and Technology, 2018, 69(3): 368-379.
[11] 闵超, DING Y, 李江, 等. 单篇论著的引文扩散 [J]. 情报学报, 2018, 37(4): 341-350.
[12] KISS I Z, BROOM M, CRAZE P G, et al. Can epidemic models describe the diffusion of topics across disciplines? [J]. Journal of informetrics, 2010, 4(1): 74-82.
[13] CHEN C, HICKS D. Tracing knowledge diffusion [J]. Scientometrics, 2004, 59(2): 199-211.
[14] JAFFE A B, TRAJTENBERG M. Flows of knowledge from universities and federal laboratories: modeling the flow of patent citations over time and across institutional and geographic boundaries [J]. Proceedings of the National Academy of Sciences, 1996, 93(23): 12671-12677.
[15] TIJSSEN R J W. Global and domestic utilization of industrial relevant science: patent citation analysis of science-technology interactions and knowledge flows [J]. Research policy, 2001, 30(1): 35-54.
[16] ZHU Y, YAN E. 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.
[17] VAN LEEUWEN T, TIJSSEN R. Interdisciplinary dynamics of modern science: analysis of cross-disciplinary citation flows [J]. Research evaluation, 2000, 9(3): 183-187.
[18] YAN E. Disciplinary knowledge production and diffusion in science [J]. Journal of the Association for Information Science and Technology, 2016, 67(9): 2223-2245.
[19] SILVA F N, RODRIGUES F A, OLIVEIRA O N, et al. Quantifying the interdisciplinarity of scientific journals and fields [J]. Journal of informetrics, 2013, 7(2): 469-477.
[20] Clarivate Analytics. GIPP mapping table [EB/OL]. [2018-10-08]. http://ipscience-help.thomsonreuters.com/inCites2Live/indicatorsGroup/aboutHandbook/appendix/mappingTable.html.
[21] GROSS P L, GROSS E M. College libraries and chemical education [J]. Science, 1927, 66(1713): 385-389.
[22] LU C, DING Y, ZHANG C. Understanding the impact change of a highly cited article: a content-based citation analysis [J]. Scientometrics, 2017, 112(2): 927-945.
[23] ZENG A, SHEN Z, ZHOU J, et al. The science of science: from the perspective of complex systems [J]. Physics reports, 2017, 714-715: 1-73.
[24] 梁国强,侯海燕,任佩丽,等. 高质量论文使用次数与被引次数相关性的特征分析 [J]. 情报杂志, 2018, 37(4): 147-153.
[25] OECD. OECD science, technology and industry scoreboard 2017: the digital transformation [EB/OL]. [2018-10-08]. https://read.oecd-ilibrary.org/science-and-technology/oecd-science-technology-and-industry-scoreboard-2017_9789264268821-en#page1.
[26] IOANNIDIS J P, BOYACK K W, SMALL H, et al. Bibliometrics:is your most cited work your best? [J]. Nature, 2014, 514(7524): 561-562.
[27] HIRSCH J E. An index to quantify an individual’s scientific research output [J]. Proceedings of the National Academy of Sciences of the United States of American, 2005, 102(46): 16569-16572.
[28] EGGHE L. Theory and practise of the g-index [J]. Scientometrics, 2013, 69(1): 131-152.
[29] GARFIELD E. The history and meaning of the journal impact factor [J]. Journal of the American Medical Association, 2006, 295(1): 90-93.
[30] BERGSTROM C. Eigenfactor: measuring the value and prestige of scholarly journals [J]. College & research libraries news, 2007, 68(5): 314-316.
[31] 任胜利. 基于引证网络分析期刊和论文的重要性 [J]. 中国科技期刊研究, 2009, 20(3): 415-418.
[32] FOSTER J G, RZHETSKY A, EVANS J A. Tradition and innovation in scientists’ research strategies [J]. American sociological review, 2015, 80(5): 875-908.
[33] WANG J, VEUGELERS R, STEPHAN P. Bias against novelty in science: a cautionary tale for users of bibliometric indicators [J]. Research policy, 2017, 46(8): 1416-1436.