Journal of Informetrics最新文献

英文中文

Is higher team gender diversity correlated with better scientific impact? 更高的团队性别多样性是否与更好的科学影响相关？

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-04-05 DOI: 10.1016/j.joi.2025.101662

Chengzhi Zhang, Jiaqi Zeng, Yi Zhao

Collaborative research involving scholars of various genders constitutes a prominent theme in scientific research that has garnered substantial attention. While several studies have investigated the connection between gender-specific collaboration patterns and the scientific impact of paper, the specific gender diversity factors that contribute to enhanced scientific impact remain largely unexplored. In this study, we analyze the correlation between gender diversity and the scientific impact of papers using the examples of Natural Language Processing (NLP) and Library and Information Science (LIS) domains. Our findings reveal three key observations: First, significant gender disparities exist in both NLP and LIS domains, with underrepresentation of female scholars. The gender disparity is more pronounced in the NLP domain compared to the LIS domain. Second, based on papers from the NLP and LIS domains, we find that papers with different gender compositions achieve varying numbers of citations, with mixed-gender collaborations gradually obtaining higher average citation counts compared to same-gender collaborations. Lastly, there is an inverted U-shaped relationship between the gender diversity of paper collaborations and the number of citations received by those papers. Based on the most impactful gender diversity calculations, the ideal gender ratio for NLP and LIS teams within a range where one gender constitutes 5% to 15% of the total number of authors. This paper contributes to the exploration of the most impactful gender diversity in collaborative research and offers insights to guide more effective scientific paper collaboration.

涉及不同性别学者的合作研究是科学研究中的一个突出主题，已经获得了大量关注。虽然有几项研究调查了特定性别的合作模式与论文的科学影响之间的联系，但有助于增强科学影响的特定性别多样性因素在很大程度上仍未得到探索。在本研究中，我们以自然语言处理（NLP）和图书馆与信息科学（LIS）领域为例，分析了性别多样性与论文科学影响的相关性。我们的研究结果揭示了三个关键观察结果：首先，在NLP和LIS领域都存在显著的性别差异，女性学者的代表性不足。与LIS领域相比，NLP领域的性别差异更为明显。其次，基于NLP和LIS领域的论文，我们发现不同性别组成的论文获得不同的引用数，混合性别合作的平均引用数逐渐高于同性合作。最后，论文合作的性别多样性与论文被引次数呈倒u型关系。根据最具影响力的性别多样性计算，NLP和LIS团队的理想性别比例是一种性别占作者总数的5%至15%。本文有助于探索合作研究中最具影响力的性别多样性，并为指导更有效的科学论文合作提供见解。

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引用次数: 0

Annotating scientific uncertainty: A comprehensive model using linguistic patterns and comparison with existing approaches 科学不确定性注释：一个使用语言模式的综合模型，并与现有方法进行比较

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-04-03 DOI: 10.1016/j.joi.2025.101661

Panggih Kusuma Ningrum , Philipp Mayr , Nina Smirnova , Iana Atanassova

We present UnScientify,¹ a system designed to detect scientific uncertainty in scholarly full text. The system utilizes a weakly supervised technique to identify verbally expressed uncertainty in scientific texts and their authorial references. The core methodology of UnScientify is based on a multi-faceted pipeline that integrates span pattern matching, complex sentence analysis and author reference checking. This approach streamlines the labeling and annotation processes essential for identifying scientific uncertainty, covering a variety of uncertainty expression types to support diverse applications including information retrieval, text mining and scientific document processing. The evaluation results highlight the trade-offs between modern large language models (LLMs) and the UnScientify system. UnScientify, which employs more traditional techniques, achieved superior performance in the scientific uncertainty detection task, attaining an accuracy score of 0.808. This finding underscores the continued relevance and efficiency of UnScientify's simple rule-based and pattern matching strategy for this specific application. The results demonstrate that in scenarios where resource efficiency, interpretability, and domain-specific adaptability are critical, traditional methods can still offer significant advantages.

我们提出了UnScientify，1一个系统，旨在检测学术全文中的科学不确定性。该系统利用弱监督技术来识别科学文本及其作者参考文献中口头表达的不确定性。UnScientify的核心方法论是基于一个多面管道，它集成了跨模式匹配、复杂句子分析和作者参考检查。该方法简化了识别科学不确定性所必需的标记和注释过程，涵盖了各种不确定性表达类型，以支持包括信息检索、文本挖掘和科学文档处理在内的各种应用。评估结果突出了现代大型语言模型（llm）和UnScientify系统之间的权衡。UnScientify采用更传统的技术，在科学不确定度检测任务中取得了更优异的成绩，准确率得分为0.808。这一发现强调了UnScientify简单的基于规则和模式匹配策略对该特定应用程序的持续相关性和效率。结果表明，在资源效率、可解释性和特定于领域的适应性至关重要的场景中，传统方法仍然可以提供显著的优势。

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引用次数: 0

Hyperprolific authorship: Unveiling the extent of extreme publishing in the ‘publish or perish’ era 超级多产作者：揭示“出版或灭亡”时代极端出版的程度

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-04-01 DOI: 10.1016/j.joi.2025.101658

Giovanni Abramo , Ciriaco Andrea D'Angelo

The increasing pressure of the “publish or perish” academic culture has contributed to the rise of hyperprolific authors—researchers who produce an exceptionally high number of publications. This study investigates the global phenomenon of hyperprolific authorship by analyzing the bibliometric data of over two million scholars across various disciplines from 2017 to 2019. Using field-specific thresholds to identify hyperprolific authors, we explore their geographic and disciplinary distributions, the impact of their publications, and their collaboration patterns. The results reveal that hyperprolific authors are concentrated in fields such as Clinical Medicine, Biomedical Research, and Chemistry, and in countries with substantial research investments, including China, the United States, and Germany. Contrary to concerns about a trade-off between quantity and quality, hyperprolific authors tend to produce higher-impact publications on average compared to their peers. Their output is strongly associated with extensive co-authorship networks, reflecting the role of collaboration in enabling prolific publishing. The findings underscore the need for balanced evaluation metrics that prioritize both quality and integrity in academic publishing. This study contributes to understanding the drivers and consequences of hyperprolific behavior, offering insights for research policy and evaluation practices.

“要么发表，要么灭亡”的学术文化的压力越来越大，这促成了高产作者——发表了大量论文的研究人员——的崛起。本研究通过分析2017年至2019年200多万名不同学科学者的文献计量数据，探讨了全球超级高产作者现象。使用特定领域的阈值来识别高产作者，我们探索了他们的地理和学科分布，他们的出版物的影响，以及他们的合作模式。结果显示，高产作者集中在临床医学、生物医学研究和化学等领域，以及在研究投资大量的国家，包括中国、美国和德国。与对数量和质量之间权衡的担忧相反，与同行相比，高产作者通常会发表更有影响力的文章。它们的产出与广泛的合著网络密切相关，反映了协作在实现多产出版方面的作用。研究结果强调了在学术出版中需要平衡的评估指标，优先考虑质量和诚信。本研究有助于理解高产行为的驱动因素和后果，为研究政策和评估实践提供见解。

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引用次数: 0

How do academic gatherings promote knowledge production and dissemination? 学术聚会如何促进知识的生产和传播？

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-03-26 DOI: 10.1016/j.joi.2025.101659

Li Hou , Ruilu Yang

Academic gatherings, such as conferences, are one of the most important types of activities in academia, yet they are understudied in the literature. Using a sample of 5379 articles published between 2000 and 2019 in the top five Economics journals, we identify whether the article was presented in academic gatherings based on wording in the paper's Acknowledgement sections, and explore its effect on citations. We find that papers acknowledging academic gatherings are associated with higher citations, and this relationship is mediated by a broader knowledge base and the more attention; that is, acknowledging academic gatherings corresponds to the paper having a broader knowledge base and being viewed more frequently, thus receive higher citations. We also find that papers authored by junior scholars with limited knowledge, skills, and experience benefit more from academic gatherings than those by senior scholars. Our findings deepen the understanding of academic gatherings and knowledge production and dissemination.

学术会议等学术集会是学术界最重要的活动类型之一，但文献对其研究不足。我们以 2000 年至 2019 年间发表在五大经济学期刊上的 5379 篇文章为样本，根据论文 "致谢 "部分的措辞识别文章是否在学术集会上发表，并探讨其对引用率的影响。我们发现，鸣谢学术集会的论文与更高的引用率相关，而这种关系是以更广泛的知识基础和更高的关注度为中介的；也就是说，鸣谢学术集会相当于论文拥有更广泛的知识基础和更高的浏览频率，从而获得更高的引用率。我们还发现，与资深学者的论文相比，知识、技能和经验有限的初级学者的论文从学术集会中获益更多。我们的发现加深了人们对学术集会和知识生产与传播的理解。

引用次数: 0

A simple equation for rank-citation profiles 一个简单的排名-引用概况方程

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-03-25 DOI: 10.1016/j.joi.2025.101660

Y.C. Tay , Akarsh Srivastava , Mostafa Rezazad , Hamid Sarbazi-Azad

There is considerable interest in the citation count for an author's publications. This has led to many proposals for citation indices to characterize rank-citation profiles, which order an author's publications by their citation count. However, there is so far no tractable model to facilitate the analysis of these profiles and the design of their indices. This paper presents a simple equation for such design and analysis.

The equation has three parameters that are calibrated by three geometrical characteristics of a rank-citation profile, namely the maximum number of citations for a publication (M), the number of cited publications (N), and the Hirsch index (h). The equation's simple form makes it tractable for analyzing rank-citation profiles and indices.

To demonstrate, the equation is used to derive closed-form approximations (in terms of M, N and h) for various indices; these expressions provide new insight into previous index analyses, the influence of a profile's tail, and the effect of time.

人们对作者的出版物的引用数很感兴趣。这导致了许多关于引文索引的建议，以表征引文排名概况，即根据作者的引文数量对其出版物进行排序。然而，到目前为止，还没有一个易于处理的模型来促进这些概况的分析和它们的指数的设计。本文给出了这种设计和分析的一个简单公式。该方程有三个参数，这些参数由排名-引文概况的三个几何特征校准，即出版物的最大被引次数(M)，被引出版物的数量(N)和赫希指数(h)。该方程的简单形式使其易于分析排名-引文概况和索引。为了证明，该方程用于推导各种指标的封闭形式近似（以M， N和h表示）；这些表达式为以前的指数分析、曲线尾部的影响以及时间的影响提供了新的见解。

引用次数: 0

Researching deeply or broadly? The effects of scientists’ research strategies on disruptive performance over their careers 深入研究还是广泛研究？科学家的研究策略对其职业生涯中破坏性表现的影响

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-03-22 DOI: 10.1016/j.joi.2025.101657

Weiyi Ao , Libo Sheng , Xuanmin Ruan , Dongqing Lyu , Jiang Li , Ying Cheng

The research strategies scientists use can affect the efficiency and direction of scientific discovery. This study focuses on the relationships between scientists’ knowledge breadth and depth strategies and disruptive performance as well as the role career age plays in these relationships. The data were from 651,831 publications authored by 12,278 biomedical scientists from the PubMed Knowledge Graph (PKG) dataset. The two main findings are as follows: (1) U-shaped correlations were found between scientists’ knowledge breadth, depth, and disruptive performance; and (2) career age influences the relationship between knowledge depth and disruptive performance, with different impacts across various stages of a scientist's career. The findings imply that future research must consider the key role scientists’ career age plays in the relationship between research strategies and scientific performance.

科学家使用的研究策略会影响科学发现的效率和方向。本研究主要探讨科学家知识广度与深度策略与破坏性绩效的关系，以及职业年龄在这些关系中的作用。数据来自PubMed Knowledge Graph （PKG）数据集中12278名生物医学科学家撰写的651831篇出版物。主要研究结果如下：(1)科学家的知识广度、深度与颠覆性绩效呈u型相关；(2)职业年龄影响知识深度与破坏性绩效的关系，在不同职业阶段的影响不同。研究结果表明，未来的研究必须考虑科学家的职业年龄在研究策略与科学绩效之间的关系中所起的关键作用。

引用次数: 0

Navigating disruptions: The effects of the pandemic on scientific collaboration and research novelty in Hong Kong 导航中断：大流行对香港科学合作和研究新颖性的影响

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-03-12 DOI: 10.1016/j.joi.2025.101656

Rong Ni , Jue Wang

Scientific collaboration and novelty are fundamental drivers of progress in knowledge advancement and innovation. However, the emergence of the COVID-19 pandemic disrupted traditional channels of scholarly communication, potentially influencing both short- and long-term scientific endeavors. Using data from 116,942 WoS publications from Hong Kong between 2017 and 2022, this study investigates the pandemic's impact on both scientific collaboration and research novelty. Our analysis reveals that the pandemic reduced the proportion of international collaborative papers and led to a contraction in the scale of international collaboration. On the other hand, domestic collaboration increased in both scope and scale. Additionally, while the pandemic negatively affected research novelty, international collaboration significantly mitigated this impact. However, no moderating effect of collaboration was observed on high-novelty research. This study underscores the importance of maintaining a collaborative scientific community and highlights the resilience and adaptability of academic communities when facing unprecedented challenges.

科学合作和新颖性是推动知识进步和创新的根本动力。然而，新冠肺炎大流行的出现扰乱了传统的学术交流渠道，可能会影响短期和长期的科学努力。本研究利用2017年至2022年香港116,942份WoS出版物的数据，调查了疫情对科学合作和研究新颖性的影响。我们的分析表明，大流行降低了国际合作论文的比例，导致国际合作规模的收缩。另一方面，国内合作在范围和规模上都有所增加。此外，虽然大流行对研究的新颖性产生了负面影响，但国际合作大大减轻了这种影响。然而，合作对高新颖性研究没有调节作用。这项研究强调了维持一个协作的科学社区的重要性，并强调了学术界在面临前所未有的挑战时的弹性和适应性。

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引用次数: 0

Multi-agent simulation of team stability evolution: A complexity science perspective 团队稳定性演化的多智能体模拟：一个复杂性科学的视角

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-03-06 DOI: 10.1016/j.joi.2025.101655

Liang Yaqi, Hou Guisheng, Jiang Xiujuan

Drawing on the theory of complex adaptive systems, this study develops a multi-agent model of a research innovation team through the NetLogo simulation platform. The operational mechanisms of the research innovation team are delineated into three distinct processes: demand-driven collaborative mechanism, objectives-driven knowledge sharing mechanism, and outcome-driven dynamic trust mechanism. These processes describe the individual decision-making of team members and the complex interactions among them. By analyzing the evolutionary patterns of research innovation team stability under various influencing factors, this study shows that: (1) While the effects on team stability vary across different parameter settings, the underlying evolutionary patterns remain largely consistent. (2) The influences of different factors on team stability exhibit nonlinear characteristics. These findings offer theoretical insights and decision-making support for fostering stable development within research innovation teams.

本研究借鉴复杂适应系统理论，通过NetLogo仿真平台建立了科研创新团队的多智能体模型。研究将科研创新团队的运行机制划分为需求驱动的协同机制、目标驱动的知识共享机制和结果驱动的动态信任机制三个不同的过程。这些过程描述了团队成员的个人决策和他们之间复杂的相互作用。通过分析不同影响因素下科研创新团队稳定性的演化模式，研究发现：(1)虽然不同参数对团队稳定性的影响存在差异，但其底层演化模式基本一致。(2)不同因素对团队稳定性的影响呈现非线性特征。研究结果为促进科研创新团队的稳定发展提供了理论见解和决策支持。

引用次数: 0

The triangle of biomedicine framework to analyze the impact of citations on the dissemination of categories in the PubMed database 利用三角生物医学框架分析引文对PubMed数据库分类传播的影响

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-02-25 DOI: 10.1016/j.joi.2025.101648

Gerson Pech , Aleksandra Mreła , Veslava Osińska , Oleksandr Sokolov

Processing scientific literature metadata allows us to verify the assignment of articles to predefined categories. The Triangle of Biomedicine (TB) is a convenient space for considering the positions of biomedical papers according to human, animal, and molecular-cellular subdisciplines. The placement of PubMed papers in the TB using citations and, what is more interesting, the dynamics of the changing positions of papers (because of citations) have not been examined to date. This research presents a method for finding the article citation vectors of directly cited papers whose components are the MeSH terms shares offered by the PubMed database. The citation vectors allow finding the paper's position in the TB and comparing it with the original position of the publication. The analysis of sets of citation vectors enables locating their position on the translational line to show the distance between human research and animal-molecular studies. Moreover, applying information entropy, the dynamics of entropies in four different sets of articles are studied.

处理科学文献元数据使我们能够验证文章对预定义类别的分配。生物医学三角（TB）是根据人类、动物和分子细胞分支学科考虑生物医学论文位置的方便空间。使用引用的PubMed论文在TB中的位置，以及更有趣的是，论文位置变化的动态（由于引用）至今尚未得到检验。本文提出了一种以PubMed数据库提供的MeSH术语共享为组成部分的直接被引论文的引文向量查找方法。引文向量允许找到论文在TB中的位置，并将其与出版物的原始位置进行比较。对引用向量集的分析可以定位它们在翻译线上的位置，以显示人类研究与动物分子研究之间的距离。此外，利用信息熵，研究了四组不同文章的熵动态。

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引用次数: 0

New paper-by-paper classification for Scopus based on references reclassified by the origin of the papers citing them 基于引用文献来源重新分类的Scopus新论文分类

IF 3.4 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2025-02-19 DOI: 10.1016/j.joi.2025.101647

Jesús M. Álvarez-Llorente , Vicente P. Guerrero-Bote , Félix Moya-Anegón

A reference-based classification system for individual Scopus publications is presented which takes into account the categories of the papers citing those references instead of the journals in which those cited papers are published. It supports multiple assignments of up to 5 categories within the Scopus ASJC structure, but eliminates the Multidisciplinary Area and the miscellaneous categories, and it allows for the reclassification of a greater number of publications (potentially 100%) than traditional reference-based systems. Twelve variants of the system were obtained by adjusting different parameters, which were applied to the more than 3.2 million citable papers from the active Scientific Journals in 2020 indexed in Scopus. The results were analyzed and compared with other classification systems such as the original journal-based Scopus ASJC, the 2 generation-reference based M3-AWC-0.8 (Álvarez-Llorente et al., 2024), and the corresponding authors' assignment based AAC (Álvarez-Llorente et al., 2023). The different variants obtained of the classification give results that improve those used as references in multiple scientometric fields. The variation called U1-F-0.8 seems especially promising due to its restraint in assigning multiple categories, consistency with reference classifications and the fact of applying normalization processes to avoid the overinfluence of articles that have a greater number of references.

提出了一个基于参考文献的Scopus出版物分类系统，该系统考虑了引用这些参考文献的论文的类别，而不是引用这些论文发表的期刊。它支持Scopus ASJC结构中多达5个类别的多重分配，但消除了多学科领域和杂项类别，并且与传统的基于参考的系统相比，它允许对更多数量的出版物进行重新分类（可能是100%）。通过调整不同的参数，获得了系统的12个变体，并将其应用于Scopus检索的2020年活跃科学期刊中320多万篇可引用论文。将结果与其他分类系统进行分析和比较，如基于原始期刊的Scopus ASJC、基于2代参考文献的M3-AWC-0.8 （Álvarez-Llorente et al., 2024）和基于相应作者分配的AAC （Álvarez-Llorente et al., 2023）。所得到的分类的不同变体提供了在多个科学计量学领域作为参考的改进结果。被称为U1-F-0.8的变化似乎特别有希望，因为它在分配多个类别方面受到限制，与参考文献分类保持一致，并且应用规范化过程以避免具有更多参考文献的文章的过度影响。

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