摘要
[目的/意义]使用深度学习技术,从时间维度量化科学家研究主题的演化速度,并探究主题演化速度的历史发展及异质性,揭示主题演化速度与科研绩效的关系。[方法/过程]首先,基于1980—2019年全球计算机科学领域近100万科学家的论文数据,使用Doc2vec深度学习方法,测度科学家相邻年份产出论文之间的文本特征距离,测算主题演化速度;然后,剖析研究主题演化速度与科研绩效的关系,并对比不同职业发展状态的科学家在主题演化程度方面的动态发展差异。[结果/结论]研究发现,近四十年来,科学家主题演化速度逐渐放缓,全球计算机科学领域中科学家整体朝向“利用式研究”的发展状态;较低的主题演化速度能带来最好的科研绩效,主题演化速度与科研绩效之间呈现倒U型关系。在职业发展生命周期中,精英科学家的主题演化程度逐渐下降,呈现“逐渐聚焦”的主题演化模式;非精英科学家的主题演化程度在大部分时期处于上升或平稳发展状态。研究结果对学科发展评估、科学家职业发展以及科学政策制定具有一定启示意义。
[Purpose/Significance]Using deep learning techniques,this study quantifies the speed of topics evolution for scientists over time and explores the historical development and heterogeneity of this trend,as well as the relationship between it and scientific performance.[Method/Process]First,based on publication data from nearly one million computer scientists worldwide from 1980 to 2019,it adopted Doc2vec to measure the distance between text features in sets of papers produced by scientists in adjacent years and calculated the speed of topic evolution.Next,it explored the relationship between the topic evolution speed and scientific performance,and compared the differences in dynamic development of topic evolution among scientists in different career stages.[Result/Conclusion]Empirical evidence indicates that,over the past four decades,the speed of research topic evolution among scientists has gradually decreased,with the global trend in computer science to“exploitation-oriented research”.Lower speed of topic evolution can bring the optimal research performance,and the relationship between them shows an inverted U-shaped curve.In the career lifecycle,elite scientists tend to have a gradually decreasing level of topic evolution,showing a trend of focusing on specific topics,while that of non-elite scientists increasing gradually or remaining steadily in most of the period.The empirical results provide policy guidance for assessment of discipline development,scientists’career advancement,and science policy formulation.
作者
柳美君
石静
杨斯杰
步一
Liu Meijun;Shi Jing;Yang Sijie;Bu Yi(Institute for Global Public Policy,Fudan University,Shanghai 200433;School of Information Management,Nanjing University,Nanjing 210023;Center for Informationalization and Information Management Research,Peking University,Beijing 100871;Department of Information Management,Peking University,Beijing 100871)
出处
《图书情报工作》
CSSCI
北大核心
2024年第6期72-82,共11页
Library and Information Service
基金
国家自然科学基金青年基金“基于因果推断的高影响力跨学科团队早期识别研究”(项目编号:72104007)和国家自然科学基金重点项目“公共治理体系变革创新的理论与机制”(项目编号:72234001)研究成果之一。
