The mechanism of tacit knowledge transformation of academic genealogy acting on scientific paradigm

Abstract

Abstract:

The evolution of scientific paradigm is the core issue of scientific development, and the inheritance of tacit knowledge is crucial to the shaping of scientific paradigm as it continues to accumulate in the academic genealogy. Focusing on the mechanism of tacit knowledge transformation in the transmission of academic genealogy on the formation of scientific paradigm, 967 valid samples were collected through questionnaires and empirically examined by using structural equation modeling. The study shows that tacit knowledge transformation significantly contributes to scientific consensus through cognitive, methodological, and normative consensus, and then promotes scientific paradigm formation. The attribute characteristics of the discipline play a moderating role in this process, the stronger the maturity, intersectionality and openness of the discipline, the higher the effectiveness of the transformation of tacit knowledge, and the more likely it is to promote the formation of innovative scientific paradigm. The study deepens the theoretical connotation of academic genealogy and expands the rationale for analyzing the influence mechanism of tacit knowledge, which is inspirational for government departments to formulate policies on talent cultivation and discipline construction, universities and research institutions to improve academic management and promote scientific research innovations, and scholars to pursue excellence in innovations and expand the paths of their academic careers.

Key words: academic genealogy, tacit knowledge, scientific consensus, characteristics of discipline, scientific paradigm

摘要：

科学范式演进是科学发展的核心议题,而隐性知识在学术谱系中持续积淀,其传承对科学范式形塑至关重要。聚焦学术谱系传承中隐性知识转化对科学范式形成的作用机理,通过问卷调查收集967份有效样本,运用结构方程模型进行实证检验。研究表明,隐性知识转化通过认知、方法、规范等方面共识显著促进科学共识达成,进而推动科学范式形成。学科属性特征在这一过程中起调节作用,学科的成熟度、交叉融合度、开放包容度越强,隐性知识转化的效用则越高,越可能促进创新性科学范式形成。研究深化了学术谱系理论内涵,拓展隐性知识影响机制分析理路,对政府部门制定人才培养和学科建设政策、高校与科研机构改进学术管理及促进科研创新、学者个体追求卓越创新和拓展学术生涯路径具有启发意义。

关键词: 学术谱系, 隐性知识, 科学共识, 学科特征, 科学范式

CLC Number:

G301

GAO Jin, HUANG Zhong-xing, WANG Ze. The mechanism of tacit knowledge transformation of academic genealogy acting on scientific paradigm[J]. Studies in Science of Science, 2026, 44(2): 281-290.

高进, 黄种兴, 王泽. 学术谱系隐性知识转化对科学范式的作用机理[J]. 科学学研究, 2026, 44(2): 281-290.

Figures/Tables 8

References 23

[1]	仇鹏飞, 孙建军, 闵超. 科学研究中的师承关系评述与思考[J]. 图书与情报, 2018(5): 50-55, 118.
	Qiu P F, Sun J J, Min C. Review of and thoughts on mentor-ship in scientific research[J]. Library & Information, 2018(5): 50-55, 118.
[2]	Damaceno R J, Rossi L, Mugnaini R, et al. The Brazilian academic genealogy: Evidence of advisor-advisee relationships through quantitative analysis[J]. Scientometrics, 2019, 119:303-333. DOI
[3]	赵勇, 付钟梦, 王宇诺, 等. 学术谱系视角下学科知识转移网络构建与演化研究——以计算机科学为例[J]. 数据分析与知识发现, 2025, 9 (3): 28-41. DOI
	Zhao Y, Fu Z M, Wang Y N, et al. Study on the construction and evolution of disciplinary knowledge transfer networks from the perspective of academic genealogy: A case study in computer science[J]. Data Analysis and Knowledge Discovery, 2025, 9 (3): 28-41. DOI
[4]	Rossi L, Damaceno R J P, Freire I L, et al. Topological metrics in academic genealogy graphs[J]. Journal of Informetrics, 2018, 12(4):1042-1058. DOI URL
[5]	Bird A. What is scientific progress?[J]. Noûs, 2007, 41(1):64-89. DOI URL
[6]	Russell B. Logical positivism[J]. Revue Internationale de Philosophie, 1950, 4 (11): 3-19.
[7]	Lather P. Postmodernism and the human sciences[J]. The Humanistic Psychologist, 1990, 18(1): 64-84. DOI URL
[8]	眭依凡, 毛智辉. 科学范式第三次转移:大学转型发展的学术逻辑[J]. 高等教育研究, 2024, 45 (6): 12-18.
	Sui Y F, Mao Z H. The third shiftin scientific paradigms:A academic logic of university transformation and development[J]. Journal of Higher Education, 2024, 45 (6): 12-18.
[9]	Shapere D. The structure of scientific revolutions[J]. The Philosophical Review, 1964, 73(3):383-394. DOI URL
[10]	朱志昌, 肖玉, 陈林昊. 科学范式、专业分工、实用主义——库恩留给方法论多元主义的遗产[J/OL]. 系统科学学报, 1-14[2025-06-23].
	Zhu Z C, Xiao Y, Chen L H. Paradigm, specialty, pragmatism—Kuhn’s legacy to methodological pluralism[J/OL]. Journal of Systems Science, 1-14[2025-06-23].
[11]	杨晓薇, 高继平. 学术谱系的起源、现状和发展[J]. 科技管理研究, 2022, 42(11): 236-242.
	Yang X W, Gao J P. The origin, status and future of academic genealogy of scientists[J]. Science and Technology Management Research, 2022, 42(11): 236-242.
[12]	盛怡瑾, 赵勇. 科学家学术谱系的内涵、构建与测度研究述评[J]. 图书情报工作, 2023, 67(14): 109-118. DOI
	Sheng Y J, Zhao Y. The connotation, construction and measurement of scientists’ academic genealogy[J]. Library and Information Service, 2023, 67(14): 109-118. DOI
[13]	刘萍, 石湘, 邢玉艳. 卓越学者隐性知识体系建模与表示方法[J]. 情报学报, 2023, 42(8): 915-925.
	Liu P, Shi X, Xing Y Y. Modeling and representation of tacit knowledge infrastructure of outstanding scholars[J]. Journal of the China Society for Scientific and Technical Information, 2023, 42(8): 915-925.
[14]	索传军, 张璇, 李木子. 论评价参照系的内涵、作用与构建——兼论学术谱系的功能和作用[J]. 中国人民大学学报, 2022, 36 (4): 180-190.
	Suo C J, Zhang X, Li M Z. On the referential frame of academic evaluation and the function of academic genealogy[J]. Journal of Renmin University of China, 2022, 36 (4): 180-190.
[15]	Kumar D, Bhowmick P K, Paik J H. Researcher influence prediction (ResIP) using academic genealogy network[J]. Journal of Informetrics, 2023, 17(2):101392. DOI URL
[16]	冀海东, 薛海彦, 张璐, 等. 创新型人才培养视角下隐性知识共享逻辑研究——以案例行动学习法为例[J]. 科学管理研究, 2024, 42 (3): 18-25.
	Ji H D, Xue H Y, Zhang L, et al. Research on the logic of tacit knowledge sharing from the perspective of cultivating innovative talents—Taking case action learning method as an example[J]. Scientific Management Research, 2024, 42 (3): 18-25.
[17]	孙金花, 代言阁, 胡健. 导师隐性知识对研究生科研兴趣的影响——基于不同主体主导互动方式的调节效应[J]. 研究生教育研究, 2019(5): 38-44.
	Sun J H, Dai Y G, Hu J. Influence of supervisors’ tacit knowledge on research interest of postgraduates: Based on the moderating effect of interactive modes by different dominant entities[J]. Journal of Graduate Education, 2019(5): 38-44.
[18]	Yong W K, Husin M M, Kamarudin S. Understanding research paradigms: A scientific guide[J]. Journal of Contemporary Issues in Business and Government, 2021, 27(2):5857-5865.
[19]	索传军, 肖玥. 交叉学科的学科特征分析与评价[J]. 中国人民大学学报, 2023, 37 (6): 132-143.
	Suo C J, Xiao Y. Analysis and evaluation of the inter-disciplinary subject based on its characteristics[J]. Journal of Renmin University of China, 2023, 37 (6): 132-143.
[20]	吴杨. 博士生教育内涵式发展中关键培养环节分析——基于个体隐性知识增长的视角[J]. 江苏高教, 2021(4): 40-45.
	Wu Y. An analysis of key training links in the intensive development of doctoral education from the perspective of individual tacit knowledge growth[J]. Jiangsu Higher Education, 2021(4): 40-45.
[21]	Jorm A. Expert Consensus in Science[M]. Singapore: Springer Nature Singapore Pte Ltd,2025:17-38.
[22]	Wang Z, Chen J, Chen J, et al. Identifying interdisciplinary topics and their evolution based on BERTopic[J]. Scientometrics, 2024, 129(11):7359-7384. DOI
[23]	Mertens D M. What comes first? The paradigm or the approach?[J]. Journal of Mixed Methods Research, 2012, 6(4):255-257. DOI URL

变量	Cronbach's α	组合信度	AVE	区分效度
隐性知识转化	0.842	0.814	0.662	0.723
科学共识达成	0.857	0.835	0.649	0.746
学科特征	0.868	0.827	0.685	0.778
科学范式形成	0.875	0.851	0.674	0.735

变量	Cronbach's α	组合信度	AVE	区分效度
隐性知识转化	0.842	0.814	0.662	0.723
科学共识达成	0.857	0.835	0.649	0.746
学科特征	0.868	0.827	0.685	0.778
科学范式形成	0.875	0.851	0.674	0.735

指标	χ2/df	RMSEA	GFI	AGFI	CFI	TLI	IFI	PGFI	PNFI	PCFI
判断标准	<3	<0.05	>0.90	>0.90	>0.90	>0.90	>0.90	>0.50	>0.50	>0.50
模型数值	1.94	0.043	0.936	0.908	0.937	0.926	0.934	0.607	0.628	0.691

指标	χ2/df	RMSEA	GFI	AGFI	CFI	TLI	IFI	PGFI	PNFI	PCFI
判断标准	<3	<0.05	>0.90	>0.90	>0.90	>0.90	>0.90	>0.50	>0.50	>0.50
模型数值	1.94	0.043	0.936	0.908	0.937	0.926	0.934	0.607	0.628	0.691

路径	中介效应	95%置信区间	总效应	Sobel Z值
隐性知识转化-认知共识-科学范式形成	0.154^***	[0.113, 0.195]	0.617^***	5.24^***
隐性知识转化-方法共识-科学范式形成	0.142^***	[0.098, 0.187]	0.584^***	4.87^***
隐性知识转化-规范共识-科学范式形成	0.137^***	[0.091, 0.182]	0.563^***	4.56^***