Scholar's Career Switch from Academia to Industry: Mining and Analysis from AMiner

IF 4.2 3区 计算机科学 Q2 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Big Data Research Pub Date : 2024-02-19 DOI:10.1016/j.bdr.2024.100441
Zhou Shao , Sha Yuan , Yinyu Jin , Yongli Wang
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引用次数: 0

Abstract

The phenomenon of scholars switching their careers from academia to industry has become more prevalent nowadays. This paper proposes a combination approach of bibliometrics analysis and data mining to study the phenomenon from the perspective of Science of Science (SciSci). Based on the proposed methods, this paper first provides an overview of frequent companies and frequent universities as well as the exponentially increasing number of scholars under the scenario. And then, this study uncovers the excessively single patterns in South Korean scholars switches using frequent pattern mining from their papers. This paper studies the knowledge and technology transfer (KTT) and the research change of scholars by using the language model, the result of which illustrates that the research difference between industry and academia gradually decreases and reaches a steady state in recent years. In exploring the driving factors of the phenomenon, deep preliminary cooperation may be an essential reason, and the career switches will not promote the published amounts of papers but may benefit its academic influence. This study should, therefore, be of value to researchers wishing to study the academia-industry career switches more intensely.

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学者从学术界到工业界的职业转换:来自 AMiner 的挖掘和分析
如今,学者从学术界转向产业界的现象越来越普遍。本文提出了文献计量学分析和数据挖掘相结合的方法,从科学的科学(SciSci)的角度研究这一现象。基于所提出的方法,本文首先概述了频繁出现的公司和频繁出现的大学,以及在这种情况下呈指数级增长的学者数量。然后,本研究通过对韩国学者论文中频繁模式的挖掘,发现了韩国学者交换中过于单一的模式。本文利用语言模型研究了知识与技术转移(KTT)和学者的研究变化,研究结果表明,近年来产学研差异逐渐缩小并达到稳定状态。在探讨这一现象的驱动因素时,前期的深度合作可能是一个重要原因,职业转换不会促进论文发表量的提升,但可能有利于其学术影响力的提升。因此,本研究对希望更深入地研究学术界-产业界职业转换的研究人员应该有一定的参考价值。
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来源期刊
Big Data Research
Big Data Research Computer Science-Computer Science Applications
CiteScore
8.40
自引率
3.00%
发文量
0
期刊介绍: The journal aims to promote and communicate advances in big data research by providing a fast and high quality forum for researchers, practitioners and policy makers from the very many different communities working on, and with, this topic. The journal will accept papers on foundational aspects in dealing with big data, as well as papers on specific Platforms and Technologies used to deal with big data. To promote Data Science and interdisciplinary collaboration between fields, and to showcase the benefits of data driven research, papers demonstrating applications of big data in domains as diverse as Geoscience, Social Web, Finance, e-Commerce, Health Care, Environment and Climate, Physics and Astronomy, Chemistry, life sciences and drug discovery, digital libraries and scientific publications, security and government will also be considered. Occasionally the journal may publish whitepapers on policies, standards and best practices.
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