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Data Analysis and Knowledge Discovery  2023, Vol. 7 Issue (11): 140-157    DOI: 10.11925/infotech.2096-3467.2022.1167
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Analyzing Researchers’ Interdisciplinarity and Academic Impacts
Zhai Yujia1,2,Zhou Rui1,Li Yan1(),Mao Zhigang1
1School of Management, Tianjin Normal University, Tianjin 300382, China
2School of Information Management, Wuhan University, Wuhan 430072, China
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Abstract  

[Objective] This study explores the relationship between interdisciplinarity and individual academic impacts, aiming to promote the development of interdisciplinary research. [Methods] First, we retrieved 69,759 researchers from the Semantic Scholar database. Then, we used the Brillouin index to analyze their interdisciplinarity based on citation, publication, and collaboration. Finally, we applied the generalized propensity value matching method to examine the causal effect of interdisciplinarity on individual academic influence. [Results] For interdisciplinary citations, the publication number and h-index of researchers increased with the rise of multi-disciplinary citations. They surpassed the critical points (1.5 and 0.05) before subsequently declining. However, interdisciplinary citation does not impact the average citation number per paper. For interdisciplinary publications, as researchers published across more disciplines, the publication number and h-index showed an upward trend, while the average citation per paper showed an oscillatory increase. For interdisciplinary collaboration, the publication number of researchers steadily increases with the growth of interdisciplinary collaboration, albeit with a gradually diminishing rate of increase. However, interdisciplinary collaboration did not influence the h-index or average citation per paper. [Limitations] The study did not include the weighting of each dimension in the measurement indicators or establish an evaluative framework for a comprehensive interdisciplinary index encompassing all three dimensions. [Conclusions] Engaging in interdisciplinary research can conditionally enhance the academic impact of researchers, while different evaluation dimensions yield various results.
Key words： Interdisciplinarity      Individual Academic Influence      Generalized Propensity Score Matching      Brillouin Index     
Received: 06 November 2022      Published: 22 March 2023
ZTFLH:  G353  
Fund:National Social Science Fund of China(18CTQ027)
Corresponding Authors: Li Yan，ORCID：0000-0001-5688-9847，E-mail：happyly1026@163.com。   
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Yujia Zhai
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Zhai Yujia, Zhou Rui, Li Yan, Mao Zhigang. Analyzing Researchers’ Interdisciplinarity and Academic Impacts. Data Analysis and Knowledge Discovery, 2023, 7(11): 140-157.

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https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2022.1167     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2023/V7/I11/140

指标 计算方法 含义 衡量维度
Shannon信息熵（SH） S H = - ∑ x i ∑ x i l o g x i ∑ x i 学科类别平均分布程度（ x i是属于第 i个学科类别的数量） 多样性
Brillouin布里渊指数（BI） H = l o g N ! - ∑ ( l o g n i ! ) N 不同学科类别的概率（N是文献的数量， n i是属于第 i个学科类别的数量） 多样性
跨领域引用指数（COC） C O C = ∑ i ∈ O C i ∑ i ∈ I ? O C i 某学科所有引用文献中引用其他学科文献的比例（ i是引用文献数） 平衡性
基尼系数（GE） G E = ∑ ( 2 i - n - 1 ) x i n ∑ x i 最初用来衡量收入不平等（ i是序列指标， x i是属于第 i个学科类别的数量） 平衡性
Jaccard系数 S J ( i , j ) = c i t ( i , j ) c o c ( i ) + c o c ( j ) - c i t ( i , j ) 不同学科之间的差异性（ i与 j交集大小和 i与 j并集大小的比值） 差异性
Rao-Stirling指数 R S = 1 - ∑ i , j S i j P i P j 学科类别i、 j论文数占所有学科论文总数比例（ S i j是学科类别 i和 j的相似度， P i= x i ?/ X；X= ∑ x i） 综合性
Interdisciplinary Measurement Indicators
匹配变量 最大值 最小值 均值 中位数 标准差
学术年龄 31 6 16.802 16 7.234
合作者数量 18 200 0 44.403 19 311.787
篇均参考文献数量 5 897.4 1 34.117 20.166 69.512
Matching Variables Statistics
结果变量 最大值 最小值 均值 中位数 标准差
发文量 83 5 7.571 6 3.788
h指数 41 0 5.929 5 3.163
篇均被引量 5 897.4 1 34.112 20.166 69.497
Statistics of Individual Academic Influence Evaluation Indicators
Coefficient Std. err. z P>|z| [95% conf. interval]
引用 m1 -0.000 272 1 0.000 281 4 -0.97 0.334 -0.000 823 6 0.000 279 5
m2 -0.000 125 5 0.000 006 5 -19.31 0 -0.000 138 3 -0.000 112 8
m3 0.000 285 8 0.000 029 4 9.73 0 0.000 228 2 0.000 343 4
_cons -0.164 229 4 0.005 132 1 -32.00 0 -0.174 288 2 -0.154 170 6
发文 m1 -0.000 818 0 0.000 212 3 -3.85 0 -0.001 234 1 -0.000 401 8
m2 -0.000 003 49 0.000 004 9 -0.71 0.477 -0.000 013 1 0.000 006 12
m3 0.000 068 4 0.000 022 2 3.09 0.002 0.000 024 9 0.000 111 8
_cons -0.440 599 0 0.003 872 0 -113.79 0 -0.448 188 0 -0.433 010 1
合作 m1 -0.001 542 4 0.000 335 0 -4.60 0 -0.002 198 9 -0.000 885 8
m2 -0.000 094 7 0.000 007 74 -12.24 0 -0.000 109 8 -0.000 079 5
m3 -0.000 377 1 0.000 035 0 -10.78 0 -0.000 445 6 -0.000 308 5
_cons -0.546 552 0 0.006 109 2 -89.46 0 -0.558 525 8 -0.534 578 1
Citation， Publication， and Collaborative h-index under Matching Variable Conditions
Variable Obs Mean Std. dev. Min Max
引用 gps_1 69 759 0.550 321 9 0.018 085 7 0.000 331 0 0.747 992 8
gps_2 69 759 0.739 131 4 0.022 011 9 0.000 045 9 0.748 002 9
gps_3 69 759 0.589 700 0 0.023 910 6 0.000 003 6 0.747 994 8
发文 gps_1 69 759 0.847 527 9 0.008 741 2 0.288 036 0 0.917 504 0
gps_2 69 759 0.744 253 0 0.009 432 4 0.647 301 5 0.990 510 8
gps_3 69 759 0.385 978 7 0.009 537 8 0.302 950 4 0.928 809 5
合作 gps_1 69 759 0.485 297 2 0.014 559 0 0.014 535 9 0.628 366 6
gps_2 69 759 0.480 192 6 0.021 561 9 0.000 095 1 0.498 285 9
gps_3 69 759 0.324 401 6 0.018 794 5 0.000 015 1 0.343 558 5
Citation， Publication and Collaboration Tendency Score
Coefficient Std. err. t P>|t| [95% conf. interval]
h1 t1 2.911 257 0.584 110 4 4.98 0 1.766 401 4.056 112
t1_sq -1.099 435 0.222 879 8 -4.93 0 -1.536 279 -0.662 591 3
pscore -1.378 445 0.486 183 9 -2.84 0.005 -2.331 365 -0.425 525 7
pscore_sq 0.705 028 2 0.585 683 2 1.20 0.229 -0.442 909 7 1.852 966
t1_pscore 0.333 438 6 0.399 452 6 0.83 0.404 -0.449 487 6 1.116 365
_cons 6.306 287 0.114 305 5 55.17 0 6.082 249 6.530 326
h2 t1 2.124 777 0.486 277 0 4.37 0 1.171 675 3.077 879
t1_sq -1.048 560 0.185 549 4 -5.65 0 -1.412 236 -0.684 883 1
pscore -2.213 865 0.404 752 3 -5.47 0 -3.007 179 -1.420 551
pscore_sq 1.549 409 0.487 586 4 3.18 0.001 0.593 740 9 2.505 078
t1_pscore 1.544 248 0.332 547 8 4.64 0 0.892 455 5 2.196 041
_cons 4.835 818 0.095 160 3 50.82 0 4.649 303 5.022 332
h3 t1 -32.038 760 10.739 530 -2.98 0.003 -53.088 20 -10.989 31
t1_sq 13.005 550 4.097 895 0 3.17 0.002 4.973 683 21.037 42
pscore -67.416 350 8.939 036 0 -7.54 0 -84.936 840 -49.895 86
pscore_sq 60.958 640 10.768 440 5.66 0 39.852 510 82.064 77
t1_pscore 45.946 630 7.344 383 0 6.26 0 31.551 660 60.341 61
_cons 41.325 620 2.101 635 0 19.66 0 37.206 420 45.444 82
Regression Analysis Estimation for Models
Coefficient Std.err. t P>|t| [95% conf. interval]
h1 t2 -4.243 080 0.300 355 5 -14.13 0 -4.831 776 -3.654 384
t2_sq 6.747 839 0.180 645 6 37.35 0 6.393 774 7.101 904
pscore -6.745 648 0.265 629 3 -25.39 0 -7.266 281 -6.225 015
pscore_sq 4.965 248 0.251 024 1 19.78 0 4.473 242 5.457 255
t2_pscore 0.932 085 9 0.349 017 5 2.67 0.008 0.248 012 3 1.616 159
_cons 8.863 430 0.098 952 9 89.57 0 8.669 483 0 9.057 378
h2 t2 -3.233 905 0.252 076 1 -12.83 0 -3.727 973 -2.739 836
t2_sq 4.757 891 0.151 608 5 31.38 0 4.460 739 5.055 043
pscore -4.978 420 0.222 931 8 -22.33 0 -5.415 366 -4.541 474
pscore_sq 3.668 039 0.210 674 3 17.41 0 3.255 118 4.080 960
t2_pscore 1.581 938 0.292 916 1 5.40 0 1.007 823 2.156 053
_cons 6.652 566 0.083 047 1 80.11 0 6.489 794 6.815 338
h3 t2 -12.606 57 5.720 054 0 -2.20 0.028 -23.817 87 -1.395 278
t2_sq 13.050 48 3.440 265 0 3.79 0 6.307 563 19.793 39
pscore -21.812 52 5.058 717 0 -4.31 0 -31.727 60 -11.897 45
pscore_sq 30.813 23 4.780 572 0 6.45 0 21.443 32 40.183 14
t2_pscore -1.126 997 6.646 786 0 -0.17 0.865 -14.154 68 11.900 69
_cons 34.316 52 1.884 487 0 18.21 0 30.622 93 38.010 11
Regression Analysis Estimation for Models
Coefficient Std.err. t P>|t| [95% conf. interval]
h1 t3 2.357 703 0.380 903 2 6.19 0 1.611 134 3.104 273
t3_sq -0.583 849 2 0.182 166 9 -3.21 0.001 -0.940 896 -0.226 802 4
pscore -5.143 949 0.373 784 5 -13.76 0 -5.876 566 -4.411 332
pscore_sq 5.129 859 0.593 166 6 8.65 0 3.967 254 6.292 464
t3_pscore -0.524 847 3 0.560 199 0 -0.94 0.349 -1.622 836 0.573 141 5
_cons 7.923 871 0.081 203 8 97.58 0 7.764 712 8.083 031
h2 t3 1.924 248 0.318 200 6 6.05 0 1.300 575 2.547 920
t3_sq -0.907 722 4 0.152 179 4 -5.96 0 -1.205 994 -0.609 451
pscore -4.658 286 0.312 253 8 -14.92 0 -5.270 302 -4.046 269
pscore_sq 4.614 590 0.495 522 2 9.31 0 3.643 367 5.585 812
t3_pscore 0.532 064 2 0.467 981 5 1.14 0.256 -0.385 178 7 1.449 307
_cons 6.254 135 0.067 836 4 92.19 0 6.121 176 6.387 095
h3 t3 189.713 2 7.013 276 0 27.05 0 175.967 2 203.459 2
t3_sq -80.151 14 3.354 099 0 -23.90 0 -86.725 17 -73.577 12
pscore -42.341 10 6.882 204 0 -6.15 0 -55.830 21 -28.851 99
pscore_sq 141.152 7 10.921 520 12.92 0 119.746 5 162.558 8
t3_pscore -264.763 0 10.314 510 -25.67 0 -284.979 5 -244.546 6
_cons 19.957 74 1.495 142 0 13.35 0 17.027 26 22.888 21
Regression Analysis Estimation for Models
Relationship Between Interdisciplinarity and Publication Volume， h-Index and Average Citations per Article
Relationship Between Interdisciplinarity Publications and Publications Volume， h-Index， and Average Citations per Article
Relationship Between Interdisciplinarity Collaboration and Publications volume， h-Index， and Average Citations per Article
[1] Wagner C S, Roessner J D, Bobb K, et al. Approaches to Understanding and Measuring Interdisciplinary Scientific Research (IDR): A Review of the Literature[J]. Journal of Informetrics, 2011, 5(1): 14-26.
doi: 10.1016/j.joi.2010.06.004
[2] 李立国, 赵阔. 跨学科知识生产的类型与经验——以21世纪诺贝尔自然科学奖为例[J]. 大学教育科学, 2021, 12(5): 14-23.
[2] (Li Liguo, Zhao Kuo. Types and Experience of Interdisciplinary Knowledge Production: Examples of Nobel Prize in Natural Science in Twenty First Century[J]. University Education Science, 2021, 12(5): 14-23.)
[3] Gates A J, Ke Q, Varol O, et al. Nature’s Reach: Narrow Work Has Broad Impact[J]. Nature, 2019, 575(7781): 32-34.
doi: 10.1038/d41586-019-03308-7
[4] Sun Y, Livan G, Ma A, et al. Interdisciplinary Researchers Attain Better Long-Term Funding Performance[J]. Communications Physics, 2021, 4: Article No.263.
[5] Garfield E. Citation Analysis as a Tool in Journal Evaluation[J]. Science, 1972, 178(4060): 471-479.
doi: 10.1126/science.178.4060.471 pmid: 5079701
[6] 高志, 张志强. 个人学术影响力定量评价方法研究综述[J]. 情报理论与实践, 2016, 39(1): 133-138.
[6] (Gao Zhi, Zhang Zhiqiang. Review of Quantitative Evaluation Method for Individual Academic Influence[J]. Information Studies: Theory & Application, 2016, 39(1): 133-138.)
[7] 黄颖, 顾秀丽, 孙蓓蓓, 等. 我国交叉科学研究的演进与展望——基于学术著作视角[J]. 图书情报知识, 2022, 39(1): 61-72.
[7] (Huang Ying, Gu Xiuli, Sun Beibei, et al. The Evolution and Prospect of Interdisciplinary Research in China: From the Perspective of Academic Books[J]. Documentation, Information & Knowledge, 2022, 39(1): 61-72.)
[8] 李长玲, 刘小慧, 刘运梅, 等. 基于开放式非相关知识发现的潜在跨学科合作研究主题识别——以情报学与计算机科学为例[J]. 情报理论与实践, 2018, 41(2): 100-104.
[8] (Li Changling, Liu Xiaohui, Liu Yunmei, et al. Identifying Potential Disciplinary Collaboration Research Topics by Open Literature-Based Discovery: Taking Information Science and Computer Science as Examples[J]. Information Studies: Theory & Application, 2018, 41(2): 100-104.)
[9] 吕琦, 上官燕红, 张琳, 等. 基于文本内容自动分类的跨学科测度研究[J]. 数据分析与知识发现, 2022, 6(4): 56-67.
[9] (Lyu Qi, Shangguan Yanhong, Zhang Lin, et al. Research on Interdisciplinary Measurement Based on Automatic Text Content Classification[J]. Data Analysis and Knowledge Discovery, 2022, 6(4): 56-67.)
[10] 邱均平, 曹洁. 不同学科间知识扩散规律研究——以图书情报学为例[J]. 情报理论与实践, 2012, 35(10): 1-5.
[10] (Qiu Junping, Cao Jie. Research on the Law of Knowledge Diffusion Between Different Disciplines[J]. Information Studies: Theory & Application, 2012, 35(10): 1-5.)
[11] Yan E J. Finding Knowledge Paths Among Scientific Disciplines[J]. Journal of the Association for Information Science and Technology, 2014, 65(11): 2331-2347.
doi: 10.1002/asi.2014.65.issue-11
[12] Min C, Chen Q Y, Yan E J, et al. Citation Cascade and the Evolution of Topic Relevance[J]. Journal of the Association for Information Science and Technology, 2021, 72(1): 110-127.
doi: 10.1002/asi.v72.1
[13] 和晋飞, 房俊民. 一个跨学科性测度指标: 作者专业度[J]. 情报理论与实践, 2015, 38(5): 42-45.
[13] (He Jinfei, Fang Junmin. An Interdisciplinarity Measure Index: Specialization of Author[J]. Information Studies: Theory & Application, 2015, 38(5): 42-45.)
[14] 张琳, 孙蓓蓓, 黄颖. 跨学科合作模式下的交叉科学测度研究——以ESI社会科学领域高被引学者为例[J]. 情报学报, 2018, 37(3): 231-242.
[14] (Zhang Lin, Sun Beibei, Huang Ying. Interdisciplinarity Measurement Based on Interdisciplinary Collaborations: A Case Study on Highly Cited Researchers of ESI Social Sciences[J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(3): 231-242.)
[15] 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.
[15] (Wang Xuefeng, Zhang Jiao, Li Jia, et al. Interdisciplinary Research Teams and Interdisciplinary Gains—An Empirical Analysis Based on NSFC Major Research Plans[J]. Science Research Management, 2018, 39(4): 157-165.)
[16] 黄颖, 张琳, 孙蓓蓓, 等. 跨学科的三维测度——外部知识融合、内在知识会聚与科学合作模式[J]. 科学学研究, 2019, 37(1): 25-35.
[16] (Huang Ying, Zhang Lin, Sun Beibei, et al. Interdisciplinarity Measurement: External Knowledge Integration, Internal Information Convergence and Research Activity Pattern[J]. Studies in Science of Science, 2019, 37(1): 25-35.)
[17] Shannon C E. A Mathematical Theory of Communication[J]. The Bell System Technical Journal, 1948, 27(3): 379-423.
doi: 10.1002/bltj.1948.27.issue-3
[18] Brillouin L. Science and Information Theory[M]. New York: Academic Press, 1956.
[19] Leydesdorff L, Rafols I. Indicators of the Interdisciplinarity of Journals: Diversity, Centrality, and Citations[J]. Journal of Informetrics, 2011, 5(1): 87-100.
doi: 10.1016/j.joi.2010.09.002
[20] Hamers L, Hemeryck Y, Herweyers G, et al. Similarity Measures in Scientometric Research: The Jaccard Index Versus Salton’s Cosine Formula[J]. Information Processing & Management, 1989, 25(3): 315-318.
doi: 10.1016/0306-4573(89)90048-4
[21] Jaccard P. The Distribution of the Flora in the Alpine Zone[J]. New Phytologist, 1912, 11(2): 37-50.
doi: 10.1111/nph.1912.11.issue-2
[22] 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.
pmid: 17327202
[23] 吴小兰, 章成志. 基于社交媒体的高影响力跨学科用户发现研究[J]. 情报学报, 2017, 36(6): 618-627.
[23] (Wu Xiaolan, Zhang Chengzhi. Finding High-Impact Interdisciplinary Users Based on Social Media[J]. Journal of the China Society for Scientific and Technical Information, 2017, 36(6): 618-627.)
[24] Abramo G, D’Angelo C A, Zhang L. A Comparison of Two Approaches for Measuring Interdisciplinary Research Output: The Disciplinary Diversity of Authors vs the Disciplinary Diversity of the Reference List[J]. Journal of Informetrics, 2018, 12(4): 1182-1193.
doi: 10.1016/j.joi.2018.09.001
[25] Leydesdorff L. Diversity and Interdisciplinarity: How Can One Distinguish and Recombine Disparity, Variety, and Balance?[J]. Scientometrics, 2018, 116(3): 2113-2121.
doi: 10.1007/s11192-018-2810-y pmid: 30147205
[26] 张琳, 孙蓓蓓, 王贤文, 等. 交叉科学成果影响力研究:使用数据与引用数据视角[J]. 情报学报, 2020, 39(5): 469-477.
[26] (Zhang Lin, Sun Beibei, Wang Xianwen, et al. The Impact of Interdisciplinarity: Distinct Effects on Usage and Citation[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(5): 469-477.)
[27] 张琳, 彭玉杰, 杜会英, 等. 技术会聚:内涵、现状与测度——兼论与学科交叉的关系[J]. 图书情报工作, 2021, 65(1): 91-101.
doi: 10.13266/j.issn.0252-3116.2021.01.014
[27] (Zhang Lin, Peng Yujie, Du Huiying, et al. Technology Convergence: Connotation, Status, Measurement, and the Relationship with Interdisciplinary Science[J]. Library and Information Service, 2021, 65(1): 91-101.)
doi: 10.13266/j.issn.0252-3116.2021.01.014
[28] Kim H, Park H, Song M. Developing a Topic-Driven Method for Interdisciplinarity Analysis[J]. Journal of Informetrics, 2022, 16(2): Article No.101255.
[29] 李江. “跨学科性”的概念框架与测度[J]. 图书情报知识, 2014(3): 87-93.
[29] (Li Jiang. The Concept and Measurement of Interdisciplinarity[J]. Documentation, Information & Knowledge, 2014(3): 87-93.)
[30] 谷志远. 高校青年教师学术产出绩效影响因素的实证研究——基于个性特征和机构因素的差异分析[J]. 高教探索, 2011(1): 129-136.
[30] (Gu Zhiyuan. An Empirical Study on the Influencing Factors of Academic Output Performance of Young Teachers in Colleges and Universities—Based on the Difference Analysis of Personality Characteristics and Institutional Factors[J]. Higher Education Exploration, 2011(1): 129-136.)
[31] Hu Z G, Chen C M, Liu Z Y. How are Collaboration and Productivity Correlated at Various Career Stages of Scientists?[J]. Scientometrics, 2014, 101(2): 1553-1564.
doi: 10.1007/s11192-014-1323-6
[32] Levin K A. Study Design III: Cross-Sectional Studies[J]. Evidence-Based Dentistry, 2006, 7(1): 24-25.
doi: 10.1038/sj.ebd.6400375 pmid: 16557257
[33] Blackburn R T, Bieber J P, Lawrence J H, et al. Faculty at Work: Focus on Research, Scholarship, and Service[J]. Research in Higher Education, 1991, 32(4): 385-413.
doi: 10.1007/BF00992183
[34] Costas R, van Leeuwen T N, Bordons M. A Bibliometric Classificatory Approach for the Study and Assessment of Research Performance at the Individual Level: The Effects of Age on Productivity and Impact[J]. Journal of the American Society for Information Science and Technology, 2010, 61(8): 1564-1581.
doi: 10.1002/asi.v61:8
[35] 邱均平, 曾倩. 国际合作是否能提高科研影响力——以计算机科学为例[J]. 情报理论与实践, 2013, 36(10): 1-5.
[35] (Qiu Junping, Zeng Qian. Whether International Collaboration Can Increase the Influence of Scientific Research[J]. Information Studies: Theory & Application, 2013, 36(10): 1-5.)
[36] 袭继红, 韩玺, 吴倩倩. 国际合作对论文影响力提升的作用研究——以外科学为例[J]. 情报杂志, 2015, 34(1): 92-95.
[36] (Xi Jihong, Han Xi, Wu Qianqian. The Enhancing Effect of International Cooperation on the Academic Impact of Scientific Papers—Taking Surgery Subject as an Example[J]. Journal of Intelligence, 2015, 34(1): 92-95.)
[37] 陶立方, 杜利民. 文后参考文献数量与影响因子的关系探究[J]. 浙江师范大学学报(自然科学版), 2020, 43(1): 115-120.
[37] Tao Lifang, Du Limin. Research on the Relationship Between the Number of References and the Impact Factor[J]. Journal of Zhejiang Normal University (Natural Sciences), 2020, 43(1): 115-120.)
[38] 刘智锋, 马永强, 杨金庆. 引文学科多样性与论文影响力的关系研究[J]. 情报杂志, 2020, 39(7): 190-195.
[38] (Liu Zhifeng, Ma Yongqiang, Yang Jinqing. Research on Relationship Between Diversity of References and Impact of Papers[J]. Journal of Intelligence, 2020, 39(7): 190-195.)
[39] Levitt J M, Thelwall M. The Most Highly Cited Library and Information Science Articles: Interdisciplinarity, First Authors and Citation Patterns[J]. Scientometrics, 2009, 78(1): 45-67.
doi: 10.1007/s11192-007-1927-1
[40] Abramo G, D’Angelo C A, Di Costa F. Do Interdisciplinary Research Teams Deliver Higher Gains to Science?[J]. Scientometrics, 2017, 111(1): 317-336.
doi: 10.1007/s11192-017-2253-x
[41] 张培, 阮选敏, 吕冬晴, 等. 人文社会科学学者的跨学科性对被引的影响研究[J]. 情报学报, 2019, 38(7): 675-687.
[41] (Zhang Pei, Ruan Xuanmin, Lyu Dongqing, et al. Research on the Relationship Between the Interdisciplinarity of Scholars and Its Impact on Citation in the Humanities and Social Sciences[J]. Journal of the China Society for Scientific and Technical Information, 2019, 38(7): 675-687.)
[42] Wang J, Thijs B, Glänzel W. Interdisciplinarity and Impact: Distinct Effects of Variety, Balance, and Disparity[J]. PLoS One, 2015, 10(5): Article No.e0127298.
[43] Rinia E J, van Leeuwen T N, van Raan A F J. Impact Measures of Inter Disciplinary Research in Physics[J]. Scientometrics, 2002, 53(2): 241-248.
doi: 10.1023/A:1014856625623
[44] Ponomarev I V, Lawton B K, Williams D E, et al. Breakthrough Paper Indicator 2.0: Can Geographical Diversity and Interdisciplinarity Improve the Accuracy of Outstanding Papers Prediction?[J]. Scientometrics, 2014, 100(3): 755-765.
doi: 10.1007/s11192-014-1320-9
[45] Mirnezami S R, Beaudry C, Larivière V. What Determines Researchers’ Scientific Impact? A Case Study of Quebec Researchers[J]. Science and Public Policy, 2016, 43(2): 262-274.
doi: 10.1093/scipol/scv038
[46] Biancani S, McFarland D A. Social Networks Research in Higher Education[A]//Paulsen M. Higher Education: Handbook of Theory and Research[M]. Dordrecht: Springer, 2013: 151-215.
[47] Yegros-Yegros A, Rafols I, D’Este P. Does Interdisciplinary Research Lead to Higher Citation Impact? The Different Effect of Proximal and Distal Interdisciplinarity[J]. PLoS One, 2015, 10(8): Article No.e0135095.
[48] Chakraborty T. Role of Interdisciplinarity in Computer Sciences: Quantification, Impact and Life Trajectory[J]. Scientometrics, 2018, 114(3): 1011-1029.
doi: 10.1007/s11192-017-2628-z
[49] 王筱纶, 赵宇翔, 王曰芬. 倾向得分匹配法: 促进数据科学视角下情报学研究的因果推断[J]. 情报学报, 2020, 39(11): 1191-1203.
[49] (Wang Xiaolun, Zhao Yuxiang, Wang Yuefen. Propensity Score Matching: Facilitating the Causal Inference of Data Science-Oriented Information Studies[J]. Journal of the China Society for Scientific and Technical Information, 2020, 39(11): 1191-1203.)
[50] Hirano K, Imbens G W. The Propensity Score with Continuous Treatments[A]//Gelman A, Meng X L. Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives: An Essential Journey with Donald Rubin’s Statistical Family[M]. Chichester, UK: John Wiley & Sons, Ltd, 2005: 73-84.
[51] 康志勇, 张宁, 汤学良, 等. “减碳”政策制约了中国企业出口吗[J]. 中国工业经济, 2018(9): 117-135.
[51] (Kang Zhiyong, Zhang Ning, Tang Xueliang, et al. Does the Policy of “Reducing Carbon” Restrict the Export of Chinese Enterprises[J]. China Industrial Economics, 2018(9): 117-135.)
[52] 周衍平, 耿芳菲, 陈会英, 等. R&D投入真的能提升企业创新绩效吗——来自广义倾向得分匹配的证据[J]. 财会月刊, 2019(20): 149-160.
[52] (Zhou Yanping, Geng Fangfei, Chen Huiying, et al. Can R&D Investment Really Improve the Innovation Performance of Enterprises—Evidence from Generalized Propensity Score Matching[J]. Finance and Accounting Monthly, 2019(20): 149-160.)
[53] Arapi-Gjini A, Möllers J, Herzfeld T. Measuring Dynamic Effects of Remittances on Poverty and Inequality with Evidence from Kosovo[J]. Eastern European Economics, 2020, 58(4): 283-308.
doi: 10.1080/00128775.2020.1720517
[54] 杨辰毓妍. 医学领域学科交叉性和论文影响力的关系研究[D]. 北京: 北京协和医学院, 2021.
[54] (Yang Chenyuyan. A Study on the Relationship Between Interdisciplinarity and Impact of Papers in the Medical Field[D]. Beijing: Peking Union Medical College, 2021.)
[55] Bia M, Mattei A. A Stata Package for the Estimation of the Dose-Response Function Through Adjustment for the Generalized Propensity Score[J]. The Stata Journal: Promoting Communications on Statistics and Stata, 2008, 8(3): 354-373.
doi: 10.1177/1536867X0800800303
[56] Larivière V, Gingras Y. On the Relationship Between Interdisciplinarity and Scientific Impact[J]. Journal of the American Society for Information Science and Technology, 2010, 61(1): 126-131.
doi: 10.1002/asi.v61:1
[57] Steele T W, Stier J C. The Impact of Interdisciplinary Research in the Environmental Sciences: A Forestry Case Study[J]. Journal of the American Society for Information Science, 2000, 51(5): 476-484.
doi: 10.1002/(ISSN)1097-4571
[58] Way S F, Morgan A C, Clauset A, et al. The Misleading Narrative of the Canonical Faculty Productivity Trajectory[J]. PNAS, 2017, 114(44): E9216-E9223.
[59] Amjad T, Ding Y, Xu J, et al. Standing on the Shoulders of Giants[J]. Journal of Informetrics, 2017, 11(1): 307-323.
doi: 10.1016/j.joi.2017.01.004
[60] 王思茗, 魏玉梅, 孙熊兰, 等. 学科交叉对领域知识研究的影响分析[J]. 情报资料工作, 2019, 40(4): 26-33.
[60] (Wang Siming, Wei Yumei, Sun Xionglan, et al. Analysis of the Influence of Interdisciplinary Interaction on Domain Knowledge Research[J]. Information and Documentation Services, 2019, 40(4): 26-33.)
[61] 费欣意. 开放存取期刊环境下的学术资源供给研究[D]. 南京: 南京大学, 2020.
[61] (Fei Xinyi. Research on the Supply of Academic Resources Under the Environment of Open Access Journals[D]. Nanjing: Nanjing University, 2020.)
[62] 开滨, 姚艳玲. 自然科学领域中睡美人文献跨学科特征实证研究——以SCI中6大学科为例[J]. 情报杂志, 2018, 37(8): 145-152.
[62] (Kai Bin, Yao Yanling. Empirical Study on the Interdisciplinary Characteristic of ‘Sleeping Beauties’ in the Field of Natural Science—Taking Six Disciplines of SCI as an Example[J]. Journal of Intelligence, 2018, 37(8): 145-152.)
[63] van Noorden R. Interdisciplinary Research by the Numbers[J]. Nature, 2015, 525: 306-307.
doi: 10.1038/525306a
[64] 薛晓丽. 跨学科论文零被引现象研究——基于零被引与高被引对比分析的视角[D]. 北京: 中国科学技术信息研究所, 2014.
[64] (Xue Xiaoli. The Study on Non-cited Interdisciplinary Articles the Perspective of Comparative Analysis Between Non-cited and Highly-cited Articles[D]. Beijing: Institute of Scientific and Technical Information of China, 2014.)
[65] 王晓红, 金子祺, 姜华. 跨学科团队的知识创新及其演化特征: 基于创新单元和创新个体的双重视角[J]. 科学学研究, 2013, 31(5): 732-741.
[65] (Wang Xiaohong, Jin Ziqi, Jiang Hua. Knowledge Innovation of Interdisciplinary Research Team and Evolving Features: Based on Innovation Unit and Innovation Individual Views[J]. Studies in Science of Science, 2013, 31(5): 732-741.)
[66] 李东, 童寿传, 李江. 学科交叉与科学家学术影响力之间的关系研究[J]. 数据分析与知识发现, 2018, 2(12):1-11.
[66] (Li Dong, Tong Shouchuan, Li Jiang. Analyzing Interdisciplinarity and Scientists' Academic Impacts[J]. Data Analysis and Knowledge Discovery, 2018, 2(12):1-11.)
[67] Berchicci L. Towards an Open R&D System: Internal R&D Investment, External Knowledge Acquisition and Innovative Performance[J]. Research Policy, 2013, 42(1): 117-127.
doi: 10.1016/j.respol.2012.04.017
[68] Freeman L C. The Gatekeeper, Pair-Dependency and Structural Centrality[J]. Quality and Quantity, 1980, 14(4): 585-592.
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