Attribute Measurement on the Academic Expertise of Scientific Researchers and the Application

Chen Chong; Wang Jiayi; Wang Yifan; Li Chunqiu

doi:10.13266/j.issn.0252-3116.2025.21.007

Library and Information Service >

2025 , Vol. 69 >Issue 21: 83 - 93

DOI: https://doi.org/10.13266/j.issn.0252-3116.2025.21.007

RESEARCH PAPERS

Attribute Measurement on the Academic Expertise of Scientific Researchers and the Application

Chen Chong ,
Wang Jiayi ,
Wang Yifan ,
Li Chunqiu

Expand

1 School of Government, Beijing Normal University, Beijing 100875;
2 Library and Information Center, Fudan University, Shanghai 200433

Chen Chong, Professor, PhD; Wang Jiayi, Master’s Candidate; Wang Yifan, Master’s Candidate; Li Chunqiu, Associate Professor, PhD, Corresponding Author, E-mail: lichunqiu@bnu.edu.cn.

Received date: 2024-11-05

Revised date: 2025-02-17

Online published: 2025-11-11

Supported by

This work is supported by the 2024 open Funds of the TIE lab of ISTIC project titled “Research on Academic Features of Researchers based on Fine-grained Expertise Discovery”;the National Social Science Foundation of China project titled “Research on Multidimensional Academic Expertise Recognition and Attribute Measurement for Quantitative Evaluation of Scientific Researchers”(Grant No. 21BTQ065).

Fold

Abstract

[Purpose/Significance] Academic expertise provides a detailed description of researchers’ knowledge characteristics, encompassing dimensions such as type, identification, and attributes. The types include the familiar research problems or the adept methods. The identifications derive from knowledge entities corresponding to specific problems or methods. The attributes are metrics that assess various aspects of expertise. Measuring these attributes is essential for expert recommendation, research team formation, talent selection, and evaluation. Therefore, this paper investigates the definition, calculation, and application of these attributes, in order to fully understand and make good use of the knowledge characteristics of researchers. [Method/Process] The study proposed or refined definitions and metrics for expertise attributes, i.e. the degree of authority, focus, and innovation. Filtering Chinese scholars in the machine learning field from the Papers with Code dataset, the research extracted problem and method-related knowledge entities from their papers. These entities were clustered first and then normalized with ChatGPT to identify expertise. Subsequently, the study measured the attributes of each expertise. The distribution of these attributes were analyzed, and external data were referenced to validate the rationality and practical value of the proposed methods. [Result/Conclusion] Empirically, most Chinese scholars in this field exhibit a few prominent expertise, reflecting their unique advantages and aligning with the natural law that “every talent has its strengths and weaknesses.” They show higher focus on research problems than on research methods, indicating a tendency to apply methods to solve problems rather than focusing on studying methods themselves. Consequently, more individuals have high-level expertise in research problems than in methods. Practically, the study defines eight combinations based on attributes to classify research communities in specific fields according to their expertise. The study also clusters researchers of a same expertise based on attributes, providing a scientific basis for peers division.

Key words： academic expertise of scientific researcher; expertise attributes; entity metrics; talent classification; expertise utilization

Cite this article

Chen Chong , Wang Jiayi , Wang Yifan , Li Chunqiu . Attribute Measurement on the Academic Expertise of Scientific Researchers and the Application[J]. Library and Information Service, 2025 , 69(21) : 83 -93 . DOI: 10.13266/j.issn.0252-3116.2025.21.007

References

[1] 陈翀,王嘉怡,高欣妍,等.科研人员学术专长的细粒度描述模型及实证研究[J].图书情报工作, 2023, 67(17):118-128.(CHEN C, WANG J Y, GAO X Y, et al. Fine-grained description model on academic expertise of scientific researchers with an empirical study[J]. Library and information service, 2023,67(17):118-128.)
[2] 强建科,佟铁钢.人才综合能力定量评价新方法--归一化加权累加法[J].创新与创业教育, 2016, 7(5):145-148.(QIANG J K, TONG T G. A new method for quantitative evaluation of the comprehensive ability of talents:normalized weighted accumulation method[J]. Journal of innovation and entrepreneurship education, 2016, 7(5):145-148.)
[3] 董超,李正风.科技人才评价中的发展性理念--剑桥大学的案例及启示[J].科研管理, 2013, 34(S1):25-30.(DONG C,LI Z F. Developmental concept of S&T talents appraisal in China based on the cases and inspiration of Cambridge University[J].Science research management, 2013, 34(S1):25-30.)
[4] 杨月坤,查椰.国外科技人才评价经验的启示与借鉴--基于英国、美国、德国的研究[J].科学管理研究, 2020, 38(1):160-165.(YANG Y K, ZHA Y. Enlightenment and reference from the experience of foreign scientific and technological talents:a study based on Britain, the United States and Germany[J].Scientific management research, 2020, 38(1):160-165.)
[5] 徐曾旭林,谢靖,于倩倩.人才多元评价模型设计方法研究[J].数据分析与知识发现, 2021, 5(8):122-131.(XU Z X L,XIE J, YU Q Q. Designing new evaluation model for talents[J].Data analysis and knowledge discovery, 2021, 5(8):122-131.)
[6] 胡丰华,周小燕.基于潜在价值的高校人才评价指标的遴选[J].中国成人教育, 2012(23):44-46.(HU F H, ZHOU X Y.Selection of evaluation indicators for university talents based on potential value[J]. China adult education, 2012(23):44-46.)
[7] 胥彦玲,袁汝兵,王健美.情报视角下的高端人才评价体系构建及实证研究[J].现代情报, 2018, 38(7):160-164, 177.(XU Y L, YUAN R B, WANG J M. The construction and empirical study on top talent evaluation system in view of intelligence[J].Journal of modern information, 2018, 38(7):160-164, 177.)
[8] 徐佳宁,孙婧.学者个人学术成就评价指标研究述评[J].情报探索, 2018(11):122-128.(XU J N, SUN J. Review on indicators studies of individual research performance evaluation[J].Information research, 2018(11):122-128.)
[9] 徐佳宁,孙婧.中外高水平学者国际影响力比较研究[J].西南民族大学学报(人文社科版), 2019, 40(6):234-240.(XU J N, SUN J. A comparative study on the international influence of Chinese and foreign high-level scholars[J]. Journal of Southwest Minzu University (humanities and social sciences edition), 2019,40(6):234-240.)
[10] 宋培彦,程志强.肿瘤领域专家学术影响力评价方法及其实证研究[J].情报工程, 2018, 4(3):48-57.(SONG P Y, CHENG Z Q. Method of experts academic influence evaluation in the field of oncology:an empirical study[J]. Technology intelligence engineering, 2018, 4(3):48-57.)
[11] RADICCHI F, FORTUNATO S, MARKINES B, et al. Diffusion of scientific credits and the ranking of scientists[J]. Physical review E, 2009, 80(5):056103.
[12] DING Y. Applying weighted PageRank to author citation networks[J]. Journal of the American Society for Information Science and Technology, 2011, 62(2):236-245.
[13] PAGE L, BRIN S, MOTWANI R, et al. The PageRank citation ranking:bringing order to the Web[J]. Stanford Infolab, 1998,9(1):1-14.
[14] CHEN P, XIE H, MASLOV S, et al. Finding scientific gems with Google's PageRank algorithm[J]. Journal of informetrics, 2007,1(1):8-15.
[15] SENANAYAKE U, PIRAVEENAN M, ZOMAYA A. The PageRank index:going beyond citation counts in quantifying scientific impact of researchers[J]. PLoS one, 2015, 10(8):1-34.
[16] JIANG X R, SUN X P, ZHUGE H. Graph-based algorithms for ranking researchers:not all swans are white![J]. Scientometrics,2013, 96(3):743-759.
[17] MARIANI M S, MEDO M, ZHANG Y C. Identification of milestone papers through time-balanced network centrality[J].Journal of informetrics, 2016, 10(4):1207-1223.
[18] 马瑞敏,刘志芳,吕宇涵,等.基于引用评论加权的论文代表作遴选研究[J].情报学报, 2023, 42(3):279-288.(MA R M,LIU Z F, LV Y H, et al. Representative paper selection based on citation comment weighing[J]. Journal of the China Society for Scientific and Technical Information, 2023, 42(3):279-288.)
[19] 高英莲,李秀霞,刘金星.被引数量、被引质量和被引离散度结合的学术期刊影响力评价研究[J].信息资源管理学报,2019, 9(4):19-23, 30.(GAO Y L, LI X X, LIU J X. The analysis of academic journals impact evaluation based on cited quantity,cited quality and cited dispersion[J]. Journal of information resources management, 2019, 9(4):19-23, 30.)
[20] ANJAD T, DAUD A, SONG M. Measuring the impact of topic drift in scholarly networks[C]//Proceedings of the Web Conference 2018. New York:ACM, 2018:373-378.
[21] ZENG A, SHEN Z, ZHOU J, et al. Increasing trend of scientists to switch between topic[J]. Nature communications, 2019, 10(1):3439.
[22] 丁敬达,陈一帆.专注-持续-延伸:基于主题的诺贝尔奖获得者研究模式探析--以物理学领域为例[J].图书馆杂志, 2023, 42(8):100-109.(DING J D, CHEN Y F.Concentration, continuation and extension:research pattern of Nobel laureates based on the topic:an empirical analysis of physics[J]. Library journal, 2023, 42(8):100-109.)
[23] 蔡永明,长青.共词网络LDA模型的中文短文本主题分析[J].情报学报, 2018, 37(3):305-317.(CAI Y M, CHANG Q.Chinese short text topic analysis by latent Dirichlet allocation model with co-word network analysis[J]. Journal of the China Society for Scientific and Technical Information, 2018, 37(3):305-317.)
[24] 聂亚青,吴庭璋,王若佳,等.基于BERTopic模型的健康信息学主题挖掘与发展演化研究[J].情报科学, 2024, 42(4):98-110, 118.(NIE Y Q, WU T Z, WANG R J, et al. BERTopic modelbased health informatics topic mining and development evolution research[J]. Information science, 2024, 42(4):98-110, 118.)
[25] 岑咏华,王曰芬.学科主题探测与演化分析:多视角述评与展望[J].情报学报, 2023, 42(4):477-494.(CEN Y H, WANG Y F. Recent advancements in detection and evolutionary tracking of scientific topics:a multi-perspective survey and prospect[J].Journal of the China Society for Scientific and Technical Information, 2023, 42(4):477-494.)
[26] 吴欣雨,李涵昱,张智雄,等.科技文献评价中语义新颖性研究综述[J].数据分析与知识发现, 2024, 8(3):29-40.(WU X Y, LI H Y, ZHANG Z X, et al. Reviewing research on semantic novelty in sci-tech literature[J]. Data analysis and knowledge discovery, 2024, 8(3):29-40.)
[27] 宋培彦,冯超慧,龙晨翔,等.基于颠覆性指数优化的细分领域优秀科技人才发现研究[J].情报杂志, 2022, 41(5):61-65.(SONG P Y, FENG C H, LONG C X, et al. Study on discovery of outstanding scientific and technological talents in specific domains based on optimized disruptive index[J]. Journal of intelligence, 2022, 41(5):61-65.)
[28] 刘小慧,廖宇,朱曼曼.颠覆性指数用于科研评价初探[J].情报理论与实践, 2021, 44(12):34-40.(LIU X H, LIAO Y, ZHU M M. A preliminary study on the application of disruption index to scientific research evaluation[J]. Information studies:theory&application, 2021, 44(12):34-40.)
[29] 刘云,王雪静,郭栋.新时代我国科技人才分类评价体系构建研究--以中国科协人才奖励为例[J].科学学与科学技术管理, 2023, 44(11):15-26.(LIU Y, WANG X J, GUO D, et al. Research on the construction of Chinese scientific and technological talents classification evaluation system in the new era:evidence from the talent award work of China Association for Science and Technology[J]. Science of science and management of S.&T., 2023, 44(11):15-26.)
[30] 袁莎,唐杰,顾晓韬.开放互联网中的学者画像技术综述[J].计算机研究与发展, 2018, 55(9):1903-1919.(YUAN S, TANG J, GU X T, et al. A survey on scholar profiling techniques in the open Internet[J]. Journal of computer research and development,2018, 55(9):1903-1919.)
[31] 高扬,池雪花,章成志,等.杰出人才精准画像构建研究--以智能制造领域为例[J].图书馆论坛, 2019, 39(6):90-97.(GAO Y, CHI X H, ZHANG C Z, et al. Precise user profile for domain-special talents:a case study of intelligent manufacturing[J]. Library tribune, 2019, 39(6):90-97.)
[32] ZHU L, ZHANG J, CUNNINGHAM S. Domain expertise extraction for finding rising stars[J]. Scientometrics, 2022,127(9):5475-5495.
[33] NIE Y, ZHU Y, LIN Q, et al. Academic rising star prediction via scholar's evaluation model and machine learning techniques[J].Scientometrics, 2019, 120(2):461-476.
[34] DEHGHAN M, BIABANI M, ABIN A A. Temporal expert profiling:with an application to T-shaped expert finding[J].Information processing and management, 2019, 55(3):1067-1079.
[35] 逯万辉,荆林波.基于作者主题模型的学者聚类与学术影响力评价方法研究[J].情报资料工作, 2020, 41(4):60-66.(LU W H, JIN L B. Research on scholar clustering and academic influence evaluation method based on author topic model[J].Information and documentation services, 2020, 41(4):60-66.)
[36] 赵蓉英,常茹茹,滑浩益,等.学术话语权视域下学者多维评价与特征分类研究[J].情报理论与实践, 2024, 47(2):15-21,60.(ZHAO R Y, CHANG R R, HUA H Y, et al. Multidimensional evaluation and characterization of scholars in the perspective of academic discourse[J]. Information studies:theory&application,2024, 47(2):15-21, 60.)
[37] GU X, BLACKMORE K L. Developing a scholar classification scheme from publication patterns in academic science:a cluster analysis approach[J]. Journal of the Association for Information Science and Technology, 2019, 70(11), 1262-1276.
[38] ZHANG H, ZHANG C Z, WANG Y Z. Revealing the technology development of natural language processing:a scientific entitycentric perspective[J]. Information processing and management,2024, 61(1):103574.

Options

Outlines

模态框（Modal）标题

Abstract

Cite this article

References