Purpose: To give a theoretical framework to measure the relative impact of bibliometric methodology on the subfields of a scientific discipline, and how that impact depends on the method of evaluation used to credit i...Purpose: To give a theoretical framework to measure the relative impact of bibliometric methodology on the subfields of a scientific discipline, and how that impact depends on the method of evaluation used to credit individual scientists with citations and publications. The authors include a study of the discipline of physics to illustrate the method. Indicators are introduced to measure the proportion of a credit space awarded to a subfield or a set of authors.Design/methodology/approach: The theoretical methodology introduces the notion of credit spaces for a discipline. These quantify the total citation or publication credit accumulated by the scientists in the discipline. One can then examine how the credit is divided among the subfields. The design of the physics study uses the American Physical Society print journals to assign subdiscipline classifications to articles and gather citation, publication, and author information. Credit spaces for the collection of Physical Review Journal articles are computed as a proxy for physics.Findings: There is a substantial difference in the value or impact of a specific subfield depending on the credit system employed to credit individual authors.Research limitations: Subfield classification information is difficult to obtain. In the illustrative physics study, subfields are treated in groups designated by the Physical Review journals. While this collection of articles represents a broad part of the physics literature, it is not all the literature nor a random sample.Practical implications: The method of crediting individual scientists has consequences beyond the individual and affects the perceived impact of whole subfields and institutions. Originality/value: The article reveals the consequences of bibliometric methodology on subfields of a disciple by introducing a systematic theoretical framework for measuring the consequences.展开更多
The contributions of scientific researchers include personal influence and talent training achievements. In this paper, using 9964 high-quality coauthor scientific papers in English teaching research from China citati...The contributions of scientific researchers include personal influence and talent training achievements. In this paper, using 9964 high-quality coauthor scientific papers in English teaching research from China citation database from 1997 to 2016, a weighted coauthor network with variety factors is constructed. A model was proposed to calculate the author’s contribution to the research team by combining personal and network characteristics. The results reveal a variety of characteristics of the co-author networks in English teaching research field, including statistical properties, community features, and authors’ contribution to teams in this discipline.展开更多
生成式人工智能作为人工智能领域的前沿技术,正日益深刻地影响着教育领域的研究与实践。本研究以CiteSpace软件为核心工具,通过分析中国知网(CNKI)和Web of Science(WoS)数据库中2020年至2025年间的相关文献,对国内外生成式人工智能赋...生成式人工智能作为人工智能领域的前沿技术,正日益深刻地影响着教育领域的研究与实践。本研究以CiteSpace软件为核心工具,通过分析中国知网(CNKI)和Web of Science(WoS)数据库中2020年至2025年间的相关文献,对国内外生成式人工智能赋能教育研究进行了系统梳理与对比。研究结果表明,国内研究更关注高等教育和职业教育中的技术应用和伦理风险,而国外研究则更关注学术诚信、个性化学习以及技术融合中的理论创新。本研究提出了促进教育智能发展的实用建议,包括深化技术场景的应用、构建伦理治理框架以及促进教育公平和创新。展开更多
文摘Purpose: To give a theoretical framework to measure the relative impact of bibliometric methodology on the subfields of a scientific discipline, and how that impact depends on the method of evaluation used to credit individual scientists with citations and publications. The authors include a study of the discipline of physics to illustrate the method. Indicators are introduced to measure the proportion of a credit space awarded to a subfield or a set of authors.Design/methodology/approach: The theoretical methodology introduces the notion of credit spaces for a discipline. These quantify the total citation or publication credit accumulated by the scientists in the discipline. One can then examine how the credit is divided among the subfields. The design of the physics study uses the American Physical Society print journals to assign subdiscipline classifications to articles and gather citation, publication, and author information. Credit spaces for the collection of Physical Review Journal articles are computed as a proxy for physics.Findings: There is a substantial difference in the value or impact of a specific subfield depending on the credit system employed to credit individual authors.Research limitations: Subfield classification information is difficult to obtain. In the illustrative physics study, subfields are treated in groups designated by the Physical Review journals. While this collection of articles represents a broad part of the physics literature, it is not all the literature nor a random sample.Practical implications: The method of crediting individual scientists has consequences beyond the individual and affects the perceived impact of whole subfields and institutions. Originality/value: The article reveals the consequences of bibliometric methodology on subfields of a disciple by introducing a systematic theoretical framework for measuring the consequences.
基金the National Natural Science Foundation of China (No. 61402119).
文摘The contributions of scientific researchers include personal influence and talent training achievements. In this paper, using 9964 high-quality coauthor scientific papers in English teaching research from China citation database from 1997 to 2016, a weighted coauthor network with variety factors is constructed. A model was proposed to calculate the author’s contribution to the research team by combining personal and network characteristics. The results reveal a variety of characteristics of the co-author networks in English teaching research field, including statistical properties, community features, and authors’ contribution to teams in this discipline.
文摘生成式人工智能作为人工智能领域的前沿技术,正日益深刻地影响着教育领域的研究与实践。本研究以CiteSpace软件为核心工具,通过分析中国知网(CNKI)和Web of Science(WoS)数据库中2020年至2025年间的相关文献,对国内外生成式人工智能赋能教育研究进行了系统梳理与对比。研究结果表明,国内研究更关注高等教育和职业教育中的技术应用和伦理风险,而国外研究则更关注学术诚信、个性化学习以及技术融合中的理论创新。本研究提出了促进教育智能发展的实用建议,包括深化技术场景的应用、构建伦理治理框架以及促进教育公平和创新。
