图书情报工作

图书情报工作 >

2024 , Vol. 68 >Issue 6: 72 - 82

DOI: https://doi.org/10.13266/j.issn.0252-3116.2024.06.007

情报研究

科学家研究主题演化速度与科研绩效的关系研究：基于计算机科学领域的分析

  • 柳美君 ,
  • 石静 ,
  • 杨斯杰 ,
  • 步一
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  • 1 复旦大学全球公共政策研究院 上海 200433;
    2 南京大学信息管理学院 南京 210023;
    3 北京大学信息化与信息管理研究中心 北京 100871;
    4 北京大学信息管理系 北京 100871
柳美君，副研究员，博士；石静，博士研究生；杨斯杰，研究助理。

收稿日期: 2023-08-28

  修回日期: 2023-12-24

  网络出版日期: 2024-03-28

基金资助

本文系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。

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Speed of Research Topic Evolution and Scientific Performance: Evidence from Computer Science

  • Liu Meijun ,
  • Shi Jing ,
  • Yang Sijie ,
  • Bu Yi
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  • 1 Institute for Global Public Policy, Fudan University, Shanghai 200433;
    2 School of Information Management, Nanjing University, Nanjing 210023;
    3 Center for Informationalization and Information Management Research, Peking University, Beijing 100871;
    4 Department of Information Management, Peking University, Beijing 100871

Received date: 2023-08-28

  Revised date: 2023-12-24

  Online published: 2024-03-28

Supported by

This work is supported by the Youth Program of the National Natural Science Foundation of China titled “Early Recognition of Interdisciplinary Teams with High Scientific Impact Based on Causal Inference” (Grant No. 72104007) and the Key Program of the National Natural Science Foundation of China titled “Theory and Mechanisms of Public Governance System Transformation and Innovation” (Grant No. 72234001).

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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。

关键词： 研究主题; 科研绩效; Doc2vec; 深度学习; 利用式研究; 精英科学家

本文引用格式

柳美君 , 石静 , 杨斯杰 , 步一 . 科学家研究主题演化速度与科研绩效的关系研究：基于计算机科学领域的分析[J]. 图书情报工作, 2024 , 68(6) : 72 -82 . DOI: 10.13266/j.issn.0252-3116.2024.06.007

Abstract

[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.

Key words： topic evolution; research performance; Doc2vec; deep learning; exploitation-oriented research; elite scientist

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