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Questions of definition and measurement continue to constrain a consensus on the measurement of interdisciplinarity. Using Rao-Stirling (RS) Diversity sometimes produces anomalous results. We argue that these unexpected outcomes can be related to the use of “dual-concept diversity” which combines “variety” and “balance” in the definitions (ex ante). We propose to modify RS Diversity into a new indicator (DIV) which operationalizes “variety,” “balance,” and “disparity” independently and then combines them ex post. “Balance” can be measured using the Gini coefficient. We apply DIV to the aggregated citation patterns of 11,487 journals covered by the Journal Citation Reports 2016 of the Science Citation Index and the Social Sciences Citation Index as an empirical domain and, in more detail, to the citation patterns of 85 journals assigned to the Web-of-Science category “information science & library science” in both the cited and citing directions. We compare the results of the indicators and show that DIV provides improved results in terms of distinguishing between interdisciplinary knowledge integration (citing references) versus knowledge diffusion (cited impact). The new diversity indicator and RS diversity measure different features. A routine for the measurement of the various operationalization of diversity (in any data matrix) is made available online. 相似文献
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《Journal of Informetrics》2022,16(2):101255
This study explores the topic-based interdisciplinarity in the research domain of literacy. A text corpus of keywords was generated through a deep keyword generation model from abstracts of 346,387 articles published in 296 disciplines from 1917 to 2021. Dirichlet-Multinomial Regression topic modeling, interdisciplinarity indices, and network analysis were employed to analyze the collected corpus. Topic modeling uncovered 15 dominant research topics in the literacy field, as well as their up-and-down trends from 2000 to 2021. For each topic, keywords were then replaced with disciplines, and interdisciplinarity was measured using four indices: variety, balance, disparity, and diversity. Finally, the interdisciplinarity of each topic, connectivity between topics, and topic trends were comprehensively analyzed on the keyword co-occurrence network. Our methodology reaches beyond connectivity limited to a few disciplines and provides insight into the direction of collaboration between disciplines centered on a research domain. Moreover, the study's deep keyword generation model has methodological implications for forming a corpus spanning numerous disciplines as a bottom-up approach. 相似文献
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《Journal of Informetrics》2023,17(3):101425
Rousseau and Mutz argued that the existing researches on diversity measure methods, such as the Rao-Stirling index, DIV, etc., have shortcomings, and urged colleagues to find a better framework for diversity measure. Based on Shannon entropy and entropy of degree vector sum, in this contribution a new diversity measure EDVS (Entropy of Degree Vectors Sum) is proposed, which meets all requirements of variety, balance and disparity, and can directly calculate the value of diversity from the observed sample data without calculating the joint probability distribution of two random variables, or mutual information. The empirical results show that: (1) the ranking of the EDVS measure has a higher Spearman correlation coefficient with DIV and DIV* than with Shannon entropy. (2) The EDVS ranking is more relevant with DIV* than with DIV. (3) The diversity of soft science journals is higher than that of hard science journals, which indicates that the interdisciplinary research of social sciences and humanities is more common than that of hard sciences such as sciences and engineering sciences. (4) Rao-Stirling index and DIV index are more sensitive to sample size. The computational complexity of the Rao-Stirling index and DIV index is , while the computational complexity of the EDVS index is . This provides the feasibility for analyzing high-dimension networks and large data sets. Results of verification on different types of data sets show that EDVS can not only effectively measure the diversity of disciplines in interdisciplinary research, but also effectively measure the diversity of other entities. 相似文献
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随着交叉科学研究在促进社会发展重大综合性问题解决方面的优势逐渐凸显,越来越多的国家对交叉科学研究给予高度的重视与支持,如何对交叉科学的研究成果进行有效的鉴定与评估也成为科技管理部门亟待解决的重要问题。本文在传统引文指标的基础上,引入PLoS官方平台的使用数据(html浏览、xml下载及pdf下载)作为补充,综合评价交叉科学研究成果的影响力情况。以2009-2013年发表在开源期刊PLoS Computational Biology的研究论文为例,研究结果表明:(1)学科交叉水平与论文影响力之间存在一定的正向关系,学科交叉水平高的论文,对应的使用数据与引用数据要明显高于学科交叉水平较低的论文;(2)论文的使用数据与引用数据相互促进,在引用数据达到峰值时,对应的使用数据也会随之出现一定的回升;(3)学科交叉程度对使用数据与引用数据之间的相关关系也有较为显著的影响。本文从使用数据和引用数据两个维度探索交叉科学研究成果的影响力,为当前交叉科学研究成果影响力的评价提供了新的借鉴与参考。 相似文献
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量化测度研究成果的学科交叉性是交叉科学研究中的重要问题,对于理解学科交叉现象与学科发展规律具有重要意义。基于引文关系的学科多样性测度是学科交叉测度的主流方法,现有的研究大多将论文的参考文献作为一个整体来探究论文的学科交叉程度,忽略了不同章节参考文献的分布、不同参考文献的重要程度以及相同参考文献的多次引用等情况。本文尝试基于论文中不同章节的引文标注位置来探究不同章节的学科交叉程度,进而根据不同章节参考文献的重要程度来计算论文的加权学科交叉度。以2007-2016年发表在PLoS ONE期刊上的研究论文为例,研究结果表明:(1)引言(Introduction)、讨论(Discussion)、方法(Method)和结果(Results)四个章节的学科交叉程度依次降低;(2)与基于整体参考文献的学科交叉测度相比,基于引文标注位置的加权学科交叉度数值相对较低且分布更为集中。基于引文标注位置的学科交叉测度方法可以从内容的微观层面更加细致地测度论文的学科交叉水平,为交叉科学研究成果的测度和高交叉研究成果的识别提供了新的视角与思路。 相似文献
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学科交叉研究综述 总被引：13，自引：1，他引：12
[目的/意义] 学科交叉研究意义重大,通过对学科交叉理论及实践的综述,分析讨论学科交叉研究存在的问题,并提出未来的研究内容和研究思路.[方法/过程] 通过对学科交叉理论及实践相关文献的系统调研、归纳和分析,结合对学科交叉类型和交叉动力学研究的综合分析,重点对当前已有的测度学科交叉程度的引文分析指标和学科交叉度计量的实践研究进行分析.[结果/结论] 当前的学科交叉研究分为宏观态势和微观内容两个层面,研究主体内容分为学科交叉类型研究、学科交叉动力学研究以及学科交叉性测度指标研究.学科交叉的测度属性分为学科多样性和学科聚合性,其测度指标分为3类:学科多样性测度指标、学科聚合性测度指标和综合性测度指标.学科交叉度计量的实践研究可以分为3类:基于交叉度的统计指标的计量、基于社会网络指标的计量以及利用多种指标的综合性计量. 相似文献
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[目的/意义]运用深度学习技术,提出结合时间和空间特征的测度(速度、覆盖度和迂回度)方法,用于量化学者研究主题演化,从而为基于内容的学者评价提供量化依据。[方法/过程]提出三维指标框架,其中速度反映作者改变研究主题快慢的平均程度,覆盖度反映作者研究内容所覆盖的主题广度,迂回度反映作者研究路径的曲折性。使用微软学术数据集中计算机科学的作者进行实证研究,并考察学者研究主题演化的三维测度和学者学术影响力和生产力的关系。[结果/结论] 实证研究结果显示,覆盖度与总被引量和总发文量的关系为单调递减,这一特征说明聚焦于特定研究主题较为深入的作者,其发文量和影响力都较大。作者研究主题演化的"速度"和"迂回度"与总被引量、总发文量都存在先增加后减少的倒U型关系。所提出的多维度指标框架不仅可在理论上丰富科学计量学对于学者研究主题转移演化及其机制的理解,而且结合深度学习模型提出了问题的解决思路。 相似文献
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利用引文内容进行主题级学科交叉类型分析 总被引：1，自引：0，他引：1
[目的/意义]针对学科交叉宏观研究不能刻画学科交叉主题,以及学科交叉微观研究仍处于主题挖掘研究阶段的现状,从内容层面解决主题学科交叉度计算问题,并构建学科交叉分类的量化标准。[方法/过程]首先,采集学术论文并解析引文内容;利用术语集获取术语和术语主题。然后,统计引文内容中的主题术语重复率。接着,计算学科间的主题学科交叉度。最后,基于主题学科交叉度分布熵,进行分类并分析。[结果/结论]研究结果表明：①六个学科难以与医学在实践应用知识层面进行学科交叉;医学的理论基础与六个学科有明显的学科知识交叉。②学科交叉存在三种类型分别为：界内交叉、工具型交叉和界外交叉。综上,通过引文内容中的术语可以有效地计算主题学科交叉度,定量地研究学科交叉类型。 相似文献
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《The Journal of Academic Librarianship》2022,48(4):102535
The aim of this study was to examine the interdisciplinarity of research data in the science, technology, engineering, and mathematics (STEM) fields. The findings revealed that interdisciplinarity was not distributed evenly across journals serving the STEM fields. Based on the diversity of the references as measured by the Gini coefficient index, the mathematical sciences showed the greatest inequality, followed by astronomy/physics, the earth sciences, the biological sciences, and technology. Based on the number of Essential Science Indicator (ESI) fields, the biological sciences showed the greatest variety, followed by the earth sciences, technology, the mathematical sciences, chemistry, and computing, while engineering showed no variety. Lastly, based on the Leydesdorff interdisciplinarity formula outcomes, the earth sciences showed the greatest diversity, but earth sciences articles were cited in articles in fewer fields than biological sciences articles. This study contributes to the study of interdisciplinary data citation for data sharing and reuse in STEM fields with respect to the measurement of the balance, variety, and diversity of research data. 相似文献
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[目的/意义] 学科交叉文献发现是进行学科交叉研究的重要前提,从海量的文献中快速、精准地发现领域相关交叉文献有助于研究人员快速地把握领域学科交叉动态,识别领域学科交叉研究热点与前沿。提出基于Rao-Stirling指数的领域学科交叉文献发现方法,并以纳米科学与纳米技术领域为例,探讨该方法的可行性。[方法/过程] 在Web of Science数据库下载纳米科学与纳米技术领域文献,构建期刊缩写-全称-学科类别对照表,利用Python编程构建文献参考文献学科分布矩阵,利用R编程计算每篇文献的Rao-Stirling指数进行文献的学科交叉测度,根据测度结果将纳米科学与纳米技术领域文献按照学科交叉程度分为三个水平,以发现领域学科交叉文献。[结果/结论] 基于Rao-Stirling指数的领域学科交叉文献发现方法可以实现领域文献水平的学科交叉测度,并发现学科交叉文献,且该研究方法也同样可扩展到其他研究领域。 相似文献
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《Journal of Informetrics》2023,17(3):101435
How do scientific knowledge and technological knowledge interact to influence patent inventions? This study combines the “Reliance on Science in Patenting” dataset with the PATSTAT database to investigate focal patents and their paper references and patent references. A temporal investigation shows a growing tendency that patents absorb more knowledge from patents than papers. Additionally, we conduct two sets of analyses, namely, studying the knowledge flow among 35 technology fields and 39 science fields and estimating the impact of cited references on patent impact. The results show that the fields are heterogeneous in absorbing scientific knowledge and technological knowledge. The empirical models indicate that the knowledge depth of both science and technology show an inverted U-shaped relationship with patent impact, while the curve of former is steeper. The knowledge breadth of both science and technology present a U-shaped relationship with patent impact, while the curve of latter is steeper. We also explore the impact of time lags and time spread on citations and estimate their joint effects. Our study provides a new understanding of the convergence of scientific knowledge and technological knowledge to facilitate patent inventions. 相似文献
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This study investigates the convergence of two bibliometric approaches to the measurement of interdisciplinary research: one based on analyzing disciplinary diversity in the reference list of publications, the other based on the disciplinary diversity of authors of publications. In particular we measure the variety, balance, disparity and integrated diversity index of, respectively, single-author, multi-author single-field, and multi-author multi-field publications. We find that, in general, the diversity of the reference list grows with the number of fields reflected in a paper’s authors’ list and, to a lesser extent, with the number of authors being equal the number of fields. Further, we find that when fields belonging to different disciplines are reflected in the authors’ list, the disparity in the reference list is higher than in the case of fields belonging to the same discipline. However, this general tendency varies across disciplines, and noticeable exceptions are found at individual paper level. 相似文献
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[目的/意义]领域知识的跨学科交叉研究能够打破学科间的壁垒,有助于发现重大科学问题的解决方案。[研究设计/方法]基于图书情报学领域文献题录信息构建轻量级领域知识图谱,从中提取学科信息、国家信息、时间信息及其关联,采用时间与空间相结合的多维度分析方法,对学科交叉的演化进程以及国家差异进行跟踪与分析。[结论/发现]图书情报学领域内学科交叉现象日渐显著,各国家的学科交叉程度与倾向存在差异,一些目前尚不突出的交叉学科方向值得关注。[创新/价值]采用多维度视角分析学科交叉现象,相关结论可以为国家科技战略制定及学科发展规划提供有益参考。 相似文献
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[目的/意义] 跨学科研究已成为现代科学创新研究的重要范式和必然趋势，探究跨学科领域中学科的发展模式与演化路径，对于揭示跨学科领域形成与发展的动态过程具有重要意义。[方法/过程] 以眼动追踪（Eye Tracking，ET）领域为例，对文献引文关系进行提取与学科标注，构建文献和学科层面的引文关系网络；计算各学科的他引比率、他被引比率和普赖斯指数，从宏观层面分析ET领域中主要学科的跨学科发展模式；考察不同阶段内部及不同阶段之间的学科引证关系，探究不同阶段各学科在跨学科发展过程中的关系结构与角色演变；基于引文的中介中心度识别连接不同学科关系的重要文献，考察重要文献、高被引文献以及参考文献之间的引文关系，从微观层面揭示ET领域发展的具体演化路径。[结果/结论] ET领域发展经历潜伏期、发展期和成熟期三个阶段，并呈现独立型、交叉型和学习型三种学科发展模式；各学科之间的引证关系随阶段变化逐渐紧密且分布逐渐均匀，神经学、心理学和临床医学在跨学科发展和知识输出方面处于核心地位；ET领域纵向发展表现为独立型学科的基础理论创新，横向发展表现为3种类型学科的深度融合，并呈现出"独立-线性-网状"的发展路径。 相似文献
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[目的/意义]随着信息资源在数量和种类上的急剧增长,学科间的交叉融合不断涌现,快速主动地从海量信息资源中识别和判断研究主题的发展演化是实现科技创新的基础。[方法/过程]在相关理论调研的基础上,结合医学领域的资源特点,提出一种基于LDA模型的主题演化探测模型和相应的流程步骤。主要步骤包括医学主题词抽取、主题识别、主题关联、关键主题识别、关键主题的演化主路径识别、演化主路径上主题分裂、融合事件识别,实现深度、细致的主题演化分析。[结果/结论]选用乳腺癌治疗研究文献为实验案例,对判断模型进行试验并对结果进行分析验证,证实提出的技术方法具有一定的可靠性。 相似文献
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《Journal of Informetrics》2022,16(3):101310
As science is becoming more interdisciplinary and potentially more data driven over time, it is important to investigate the changing specialty structures and the emerging intellectual patterns of research fields and domains. By employing a clustering-based network approach, we map the contours of a novel interdisciplinary domain – research using social media data – and analyze how the specialty structures and intellectual contributions are organized and evolve. We construct and validate a large-scale (N = 12,732) dataset of research papers using social media data from the Web of Science (WoS) database, complementing it with citation relationships from the Microsoft Academic Graph (MAG) database. We conduct cluster analyses in three types of citation-based empirical networks and compare the observed features with those generated by null network models. Overall, we find three core thematic research subfields – interdisciplinary socio-cultural sciences, health sciences, and geo-informatics – that designate the main epicenter of research interests recognized by this domain itself. Nevertheless, at the global topological level of all networks, we observe an increasingly interdisciplinary trend over the years, fueled by publications not only from core fields such as communication and computer science, but also from a wide variety of fields in the social sciences, natural sciences, and technology. Our results characterize the specialty structures of this domain at a time of growing emphasis on big social data, and we discuss the implications for indicating interdisciplinarity. 相似文献
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Scientific research is increasingly relying on collaborations to address complex real-world problems. Many researchers, policymakers, and administrators consider a multidisciplinary environment an important factor for fostering research collaborations, especially interdisciplinary ones that involve researchers from different disciplines. However, it remains unknown whether a higher level of multidisciplinarity within an academic institution is associated with internal collaborations that are more prevalent and more interdisciplinary. Analyzing 90,000 publications by 2500 faculty members in over 100 academic institutions from three multidisciplinary areas, information, public policy, and neuroscience, we investigated the connection between multidisciplinarity and research collaborations. Based on social network analysis and text mining, our analysis suggests that more multidisciplinary institutions are not necessarily more collaborative, although they do feature collaborations that are more interdisciplinary. Our findings provide implications for academic administrators and policymakers to promote research collaborations and interdisciplinarity in academic institutions. 相似文献
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