Journal of Informetrics最新文献

Author position ratio (APR): a simple complementary bibliometric descriptor of authorship patterns 作者位置比（APR）：作者身份模式的简单补充文献计量描述符

IF 3.5 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2026-01-30 DOI: 10.1016/j.joi.2026.101776

Valentí Rull

Assessing an individual researcher’s contribution using single-number citation metrics is challenging, especially in the context of increasingly multi-authored publications. Existing indices, whether simple or composite, often overlook authorship patterns and provide limited insight into each author’s actual involvement in research and publication tasks. Attempts to standardize credit allocation, such as the CRediT taxonomy, rely on self-reported information and are prone to subjectivity, while position-weighted citation metrics typically depend on arbitrary assumptions, discipline-specific conventions, or computationally intensive modeling. This paper introduces the author position ratio (APR), a simple, semiquantitative, and field-independent descriptor specifically designed to complement citation-based indicators by capturing career-long authorship patterns. The APR classifies each scholar according to the relative prevalence of single/first, intermediate, and last authorship—corresponding to author, collaborator, and manager strategies—and expresses the dominant pattern through an alphanumeric code and a ternary graphical representation. The method is illustrated through hypothetical examples and applied to real data from the 500 paleontologists included in the 2025 Stanford ranking of highly influential scientists. The results show that APR categories reveal substantial heterogeneity in authorship strategies that remains hidden in traditional bibliometric indicators: researchers with similar h-indices or c-scores may exhibit markedly different authorship profiles. Combining APR descriptors with citation metrics enriches the interpretation of scientific performance by contextualizing impact measures within authorship roles. Because it requires only byline information, avoids arbitrary weighting schemes, and yields intuitive numerical and graphical outputs, the APR offers a transparent and easily applicable complement for evaluation practices and research assessment frameworks.

使用单号引用指标评估单个研究人员的贡献是具有挑战性的，特别是在越来越多的多作者出版物的背景下。现有的索引，无论是简单的还是综合的，往往忽略了作者模式，并且对每个作者在研究和出版任务中的实际参与提供了有限的见解。标准化信用分配的尝试，如信用分类法，依赖于自我报告的信息，容易产生主观性，而位置加权引用度量通常依赖于任意假设、特定学科的惯例或计算密集型建模。本文介绍了作者地位比（APR），这是一个简单的、半定量的、与领域无关的描述符，专门用于通过捕获整个职业生涯的作者模式来补充基于引文的指标。APR根据单一/第一作者、中间作者和最后作者的相对流行程度对每个学者进行分类——对应于作者、合作者和管理者的策略——并通过字母数字代码和三元图形表示来表达主导模式。该方法通过假设的例子加以说明，并应用于2025年斯坦福大学极具影响力科学家排名中500名古生物学家的真实数据。结果表明，APR分类揭示了作者策略的实质性异质性，这在传统文献计量指标中仍然是隐藏的：具有相似h指数或c分数的研究人员可能表现出明显不同的作者概况。将APR描述符与引文指标相结合，通过将作者角色中的影响指标置于背景中，丰富了对科学绩效的解释。由于它只需要署名信息，避免了任意的加权方案，并产生直观的数字和图形输出，因此APR为评估实践和研究评估框架提供了透明和易于应用的补充。

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引用次数: 0

Design flaws in the Polish science quality evaluation system: A technical analysis of latent properties 波兰科学质量评价体系中的设计缺陷：潜在特性的技术分析

IF 3.5 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2026-01-29 DOI: 10.1016/j.joi.2026.101773

Tomasz P. Pawlak

In 2018, Poland’s science and higher education underwent reforms introducing a new scientometrics system to periodically evaluate research quality in about 300 institutions. This system primarily relies on parametric evaluations, with limited expert input. Evaluation outcomes affect institutions’ government funding, as well as their authority to grant academic degrees and offer study fields. Initially applied in 2022 for the 2017–2021 period, the system remains largely unchanged for the 2026 evaluation of 2022–2025, sustained more by political instability than by trust in its efficacy.

This paper examines the Polish science evaluation system through the lenses of statistics, decision-making, and operations research. We reveal four design flaws causing counterintuitive results, enabling institutions to boost scores by even 60% through creative reporting, discriminating based on size, permitting result manipulation by parties with conflicting interests, and deviating markedly from current decision science standards. Our theoretical insights are validated by data from the 2022 and 2026 evaluations. Recommendations for system improvements are also presented.

2018年，波兰的科学和高等教育进行了改革，引入了一种新的科学计量体系，定期评估约300所机构的研究质量。该系统主要依赖于参数评估，专家输入有限。评估结果会影响各院校的政府资助，以及它们授予学位和提供研究领域的权力。该制度最初于2022年适用于2017-2021年，但在2022 - 2025年的2026年评估中基本保持不变，主要是由于政治不稳定，而不是对其有效性的信任。本文从统计学、决策学和运筹学的角度考察了波兰的科学评价体系。我们揭示了导致反直觉结果的四个设计缺陷，这些缺陷使机构能够通过创造性的报告、基于规模的歧视、允许利益冲突的各方操纵结果，以及明显偏离当前的决策科学标准，甚至将分数提高60%。我们的理论见解得到了2022年和2026年评估数据的验证。提出了改进系统的建议。

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引用次数: 0

Tracing scientific knowledge flow in patents: An explainable machine learning study of citation types and their temporal dynamics 追踪专利中的科学知识流动：引用类型及其时间动态的可解释机器学习研究

IF 3.5 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2026-01-29 DOI: 10.1016/j.joi.2026.101774

Yu Geng , Yixian Yin , Ruonan Cai , Xianwen Wang

Patent references to scientific publications provide essential evidence of knowledge flow from science to technology, yet existing studies have rarely examined three citation types (front-page, in-text, and overlapping) or assessed how their knowledge characteristics change over time. Prior research also tends to focus on static comparisons at the level of a limited number of features, overlooking their combined contributions to knowledge flow. This study analyzes 581,684 scientific articles in biochemistry and molecular biology from 2011 to 2020, along with 230,431 patent-paper citation links involving these articles. We integrate the three citation types into a real-versus-virtual citation framework and apply XGBoost combined with SHAP and the Gini-Simpson index to assess feature contributions and their temporal dynamics. Results indicate that paper authority (normalized citation impact) and knowledge relevance (title similarity) are consistently the most influential drivers across all citation types. Front-page citations emphasize knowledge relevance, in-text citations highlight academic prestige, and overlapping citations combine both. From 2011 to 2020, all three types shift from relevance-driven to authority-driven citation behavior, with overlapping citations exhibiting intermediate trajectories. These findings provide new evidence on heterogeneous and evolving science-technology linkages and offer methodological guidance for evaluating knowledge flows in innovation studies.

对科学出版物的专利引用提供了知识从科学流向技术的重要证据，然而现有的研究很少检查三种引用类型（头版、正文和重叠）或评估它们的知识特征如何随时间变化。先前的研究也倾向于在有限数量的特征水平上进行静态比较，而忽略了它们对知识流动的综合贡献。本研究分析了2011年至2020年生物化学和分子生物学领域的581684篇科学论文，以及涉及这些论文的230431篇专利论文引用链接。我们将这三种引文类型整合到一个真实与虚拟的引文框架中，并应用XGBoost、SHAP和Gini-Simpson指数来评估特征贡献及其时间动态。结果表明，论文权威性（规范化引文影响）和知识相关性（标题相似度）始终是所有引文类型中最具影响力的驱动因素。首页引用强调知识相关性，正文引用强调学术声望，重叠引用将两者结合起来。2011 - 2020年，三种类型的引用行为都从相关性驱动型向权威驱动型转变，重叠引用呈现中间轨迹。这些发现为研究科学技术的异质性和不断发展的联系提供了新的证据，并为评估创新研究中的知识流动提供了方法学指导。

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引用次数: 0

Assessing data quality in citation analysis: A case study of web of science and Crossref 引文分析中的数据质量评估：以web of science和Crossref为例

IF 3.5 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2026-01-29 DOI: 10.1016/j.joi.2026.101775

Guoyang Rong , Ying Chen , Thorsten Koch , Keisuke Honda

Developing methods for assessing data quality in citation analysis is crucial for ensuring the reliability, comparability, and validity of research evaluation results derived from large-scale datasets. We begin by demonstrating the substantial discrepancies between two widely used data sources, Web of Science (WoS) and CrossRef, and show that such differences can significantly affect the results of citation analyzing. To address this issue, we integrated the WoS and Crossref datasets as a showcase and developed a systematic evaluation framework to assess the data quality of WoS from two dimensions: coverage completeness and key node inclusion. The results demonstrate that the proposed method effectively analyzes publication coverage, reference completeness, and key node inclusion on three levels: dataset, publication, and cluster. The contribution of this study lies in extending data quality evaluation by enabling the assessment of whether key nodes and their citations are included in a dataset, and by systematically identifying publications affected by such omissions.

开发引文分析中评估数据质量的方法对于确保来自大规模数据集的研究评估结果的可靠性、可比性和有效性至关重要。我们首先展示了Web of Science （WoS）和CrossRef这两个广泛使用的数据源之间的巨大差异，并表明这种差异会显著影响引文分析的结果。为了解决这个问题，我们整合了WoS和Crossref数据集作为展示，并开发了一个系统的评估框架，从覆盖完整性和关键节点包含两个维度评估WoS的数据质量。结果表明，该方法在数据集、出版物和聚类三个层面上有效地分析了出版物覆盖率、参考完整性和关键节点包含情况。本研究的贡献在于通过评估关键节点及其引用是否包含在数据集中，以及通过系统地识别受此类遗漏影响的出版物，扩展了数据质量评估。

引用次数: 0

How highly cited papers affect LIS journal impact factor? An empirical study 高被引论文如何影响美国期刊影响因子？实证研究

IF 3.5 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2026-01-25 DOI: 10.1016/j.joi.2026.101771

Xiaojuan Liu , Nannan Xiang , Mu-hsuan Huang

The Journal Impact Factor (JIF) is a predominant indicator in academic evaluation. However, its validity may be compromised by the highly skewed distribution of citations within the same journal, which can lead to misinterpretations in research assessment. In light of these concerns, this study examines journals in the field of Library and Information Science (LIS) by applying different treatments of highly cited papers to evaluate their influence on JIF values and ranking outcomes. To enhance comprehension of JIF-based evaluation results, the Citation Concentration Index (CCI) is proposed, which reflects the distribution of citation counts among the papers in a journal. The analysis reveals that highly cited papers are more prevalent in top-ranked journals and exert a substantial influence on JIF, while for certain lower-ranked journals, the citation counts are all relatively low, and their JIFs are more dominated by a very small number of papers. These findings underscore that JIF-based journal rankings should be interpreted in conjunction with the citation distribution of papers within each journal.

期刊影响因子（JIF）是学术评价的重要指标。然而，它的有效性可能会受到同一期刊内引用的高度倾斜分布的影响，这可能导致研究评估中的误解。鉴于这些问题，本研究通过对高被引论文采用不同的处理方法来评估它们对JIF值和排名结果的影响，从而检查了图书馆与信息科学（LIS）领域的期刊。为了更好地理解基于jif的评价结果，本文提出了反映期刊被引频次分布的引文集中指数（CCI）。分析发现，高被引论文在排名靠前的期刊中更为普遍，对JIF的影响较大，而某些排名靠后的期刊，其被引次数都相对较低，JIF更多的是由极少数论文主导。这些发现强调，基于jif的期刊排名应该与每个期刊中论文的被引分布结合起来解释。

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引用次数: 0

Cover effects on social attention: Evidence from Cell Biology, Multidisciplinary Chemistry, and Applied Physics 覆盖效应对社会注意力的影响：来自细胞生物学、多学科化学和应用物理学的证据

IF 3.5 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2026-01-22 DOI: 10.1016/j.joi.2026.101772

Yezhu Wang , Yundong Xie , Qing Ye , Lu Guo , Rongting Zhou

Social media has transformed scholarly communication by extending research visibility beyond citations to broader public engagement. This study investigates whether cover papers attract higher social attention, as measured by altmetrics. Drawing on 1.46 million papers (2012–2024) from the disciplines of Cell Biology, Multidisciplinary Chemistry, and Applied Physics, we apply coarsened exact matching and regression models to assess the effect of cover status on altmetric indicators. The results reveal that cover papers consistently receive higher Altmetric Attention Scores than comparable non-cover papers. This effect is stronger in high-impact and open access journals (particularly in Multidisciplinary Chemistry and Applied Physics), and in journals with embedded social media plugins. To better capture the immediate dissemination and public attention surrounding research, we propose the News and Social Media Attention Score (NSAS)—a composite metric aggregating mentions from news outlets, blogs, Twitter, and Facebook. Results using the NSAS confirm the robustness of the main findings, and analysis of specific metrics indicates that Twitter is the primary source of increased attention. These findings provide valuable insights for journal editors, researchers, and policymakers seeking to enhance the societal visibility and impact of scientific research.

社交媒体通过将研究的可见性从引用扩展到更广泛的公众参与，改变了学术交流。这项研究调查了封面报纸是否吸引了更高的社会关注，正如altmetrics衡量的那样。利用细胞生物学、多学科化学和应用物理等学科的146万篇论文（2012-2024年），采用粗糙的精确匹配和回归模型评估覆盖状态对替代指标的影响。结果显示，封面论文的Altmetric注意力得分始终高于可比的非封面论文。这种效应在高影响力和开放获取期刊（特别是多学科化学和应用物理）以及嵌入社交媒体插件的期刊中更为明显。为了更好地捕捉研究的即时传播和公众关注，我们提出了新闻和社交媒体关注评分（NSAS）——一种综合指标，汇总了来自新闻媒体、博客、Twitter和Facebook的提及。使用NSAS的结果证实了主要发现的稳健性，对具体指标的分析表明，Twitter是增加关注的主要来源。这些发现为寻求提高科学研究的社会知名度和影响的期刊编辑、研究人员和政策制定者提供了有价值的见解。

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引用次数: 0

A multi-dimensional indicator system for identifying highly innovative papers: a knowledge absorption, creation, and diffusion perspective 识别高度创新论文的多维指标体系：知识吸收、创造和扩散视角

IF 3.5 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2026-01-22 DOI: 10.1016/j.joi.2025.101765

Wei Cheng , Xiaomin Zheng , Dejun Zheng, Ming Li, Tianshi Cong

Conventional metrics often emphasize a single aspect of academic innovation, such as citation counts, topical novelty, or disruption, and therefore fail to capture the multi-mechanism processes through which academic innovation emerges and spreads. This study develops a multi-dimensional indicator system grounded in a knowledge-evolution perspective that conceptualizes innovation across three stages: knowledge absorption, creation, and diffusion. Each stage corresponds to distinct, measurable traces in citation networks and textual semantics. Furthermore, the framework integrates citation- and content-based indicators and refines topic-novelty and knowledge-disruption measures through semantic and temporal modeling to reduce granularity and aggregation bias. Empirical validation is conducted using 1940 publications by Price Medal recipients and their cited and citing records (54,045 unique documents). Robustness is further examined through inter-indicator correlation analysis, comparison with citation impact, case validation using top-ranked innovative papers and representative works by Loet Leydesdorff, as well as a cross-field validation experiment. Results demonstrate that the proposed system effectively identifies highly innovative research and captures domain-specific patterns of knowledge generation and dissemination, offering a comprehensive and scalable approach for evaluating academic innovation.

传统的指标通常强调学术创新的单一方面，如引用数、主题新颖性或颠覆性，因此无法捕捉学术创新产生和传播的多机制过程。本研究基于知识演化的视角，构建了一个多维指标体系，将创新分为三个阶段：知识吸收、知识创造和知识扩散。每个阶段对应于引文网络和文本语义中不同的、可测量的痕迹。此外，该框架整合了基于引文和内容的指标，并通过语义和时间建模来细化主题新颖性和知识中断度量，以减少粒度和聚集偏差。实证验证使用了1940份由普莱斯奖章获得者发表的出版物及其引用和引用记录（54,045份独特的文件）。通过指标间相关分析、引文影响对比、利用排名靠前的创新论文和Loet Leydesdorff的代表性著作进行案例验证以及跨领域验证实验，进一步检验稳健性。结果表明，该系统有效地识别了高度创新的研究，并捕获了特定领域的知识生成和传播模式，为评估学术创新提供了一种全面和可扩展的方法。

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引用次数: 0

BiblioMerge: A Python-based automated tool to merge WoS and Scopus bibliographic data, compatible with Biblioshiny, BibExcel, VOSviewer, SciMAT and ScientoPy BiblioMerge：一个基于python的自动化工具，用于合并WoS和Scopus书目数据，与Biblioshiny， BibExcel, VOSviewer， SciMAT和ScientoPy兼容

IF 3.5 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2026-01-17 DOI: 10.1016/j.joi.2026.101770

David Diez-Junguitu , Miguel Á. Peña-Cerezo

Academic research relies predominantly on two main scientific publication databases: Web of Science and Scopus. Combining these databases is highly recommended, but it is extremely difficult and arduous (frequently a manual task) owing to their different structures and duplicate records. To streamline this operation, BiblioMerge, a custom Python-based application, has been developed. BiblioMerge allows researchers to combine data from both sources, efficiently remove duplicates, and perform author name normalisation and disambiguation, thus overcoming the limitations observed in some bibliometric programmes. In addition, BiblioMerge provides valuable assistance in the management of keywords and cited references, facilitating normalisation or grouping as required. The end result is a single, consolidated dataset in four different file formats that is directly compatible with several bibliometric software applications, including Biblioshiny, BibExcel, VOSviewer, ScientoPy, and SciMAT. It is freely accessible through the web and does not require the installation of any specific software for its use.

学术研究主要依赖于两个主要的科学出版数据库：Web of Science和Scopus。强烈建议组合这些数据库，但由于它们的结构不同和重复记录，这是非常困难和艰巨的（通常是手动任务）。为了简化此操作，开发了一个基于python的自定义应用程序BiblioMerge。BiblioMerge允许研究人员结合两个来源的数据，有效地去除重复，并执行作者姓名规范化和消歧，从而克服了在一些文献计量程序中观察到的局限性。此外，BiblioMerge在关键字和引用参考文献的管理方面提供了宝贵的帮助，促进规范化或按需分组。最终的结果是一个单一的，统一的数据集在四种不同的文件格式，是直接兼容几个文献计量软件应用程序，包括Biblioshiny， BibExcel, VOSviewer, ScientoPy，和SciMAT。它可以通过网络免费访问，并且不需要安装任何特定的软件即可使用。

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引用次数: 0

Does open access promote interdisciplinary knowledge flow? A Quasi-Natural experiment of journal transitions to fully OA in agronomy 开放获取是否促进了跨学科的知识流动？农学期刊向全OA转型的准自然实验

IF 3.5 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2026-01-10 DOI: 10.1016/j.joi.2026.101768

Xianwen Wang, Pengfei Jia, Weixi Xie

Interdisciplinary knowledge diffusion is critical for addressing complex scientific and societal problems, yet rigid disciplinary boundaries often constrain citation flows across fields. Open Access (OA) has been advocated as a mechanism to improve research visibility and accessibility, but its effect on interdisciplinary citation remains insufficiently examined. This study evaluates the impact of OA on the breadth of interdisciplinary knowledge dissemination. Exploiting a quasi-natural experiment stemming from journal transitions to fully OA in the field of agronomy (2000–2024), we analyze over 57,000 articles and 1.5 million citing records using a multilayer disciplinary classification system. Employing negative binomial regressions and difference-in-differences estimations, we find that OA significantly broadens the disciplinary scope of citations at the topic, subfield, field, and domain levels. Mechanism analysis indicates that OA enhances dissemination via social media and public platforms, facilitating greater knowledge spillovers. These results emphasize OA’s institutional function in advancing both research accessibility and interdisciplinary knowledge convergence.

跨学科的知识传播对于解决复杂的科学和社会问题至关重要，然而严格的学科界限往往限制了跨领域的引文流动。开放获取（OA）作为一种提高研究可见性和可及性的机制一直被提倡，但其对跨学科引文的影响仍未得到充分研究。本研究评估OA对跨学科知识传播广度的影响。利用农学领域期刊向完全开放获取转变的准自然实验（2000-2024），我们使用多层学科分类系统分析了57,000多篇文章和150万条引用记录。采用负二项回归和差中差估计，我们发现OA在主题、子领域、领域和领域层面显著拓宽了引文的学科范围。机制分析表明，OA增强了社交媒体和公共平台的传播，促进了更大的知识溢出。这些结果强调了OA在促进研究可及性和跨学科知识融合方面的制度功能。

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引用次数: 0

λ-index: A new indicator based on generalized law in Information Production Processes λ指数：一种基于信息生产过程广义规律的新指标

IF 3.5 2区管理学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS

Journal of Informetrics

Pub Date : 2026-01-10 DOI: 10.1016/j.joi.2026.101769

Hiran H. Lathabai

Despite the presence of skewness (i) that induces the possibility of existence of famous 80/20 law or others (like 90/10, 70/30, etc.), and (ii) that contributes to the relevance of indicators like h-type indicators, the law-indicator compliance in IPPs remained almost unexplored until recently. A recent exploration on the ability of the major existing basic h-type indicators to reflect the 80/20 rule found h-index as the best in that regard. Though considering 20 % of T (total sources/publications) or 0.2T as an indicator, provided a slightly better alternative, the need for a better indicator was also reported. In this work, we devise such an indicator, namely the λ-index, by using a generalized law viz.

(1 - ρ) / ρ

law wherein 80/20, 90/10, 70/30, etc., are special cases. The λ-index corresponds to the largest fraction of sources, ρ=λ/T, that have produced approximately (1−ρ) of all items. Interesting informetric properties of

λ

, including its linear dependence on T (total publications) that makes it remarkable and unique, are proven and discussed in comparison with other major base indicators.

λ

-index is found to exhibit indicator-law compliance for 80/20 law better than all the basic h-type indicators and 0.2T indicator in multiple datasets. Possibilities for deciding the governing specific law (like 70/30 or 80/20) in a field using

ρ

is also discussed. Upon sensitivity analysis on profile of an author with redistribution of citations (to simulate a law change),

λ

is found to be better than other indicators in maintaining law-indicator compliance, hinting its importance to be used even in profiles having high volatility.

尽管存在偏度(i)导致存在著名的80/20定律或其他定律（如90/10、70/30等）的可能性，以及（ii）导致h型指标等指标的相关性，但ipp中的法律-指标合规性直到最近才得到探索。最近一项关于现有主要基本h型指标反映80/20规则的能力的研究发现，h指数在这方面是最好的。虽然考虑到T（总来源/出版物）的20%或0.2T作为一个指标，提供了一个稍好的替代方案，但也报告了需要一个更好的指标。在这项工作中，我们利用广义定律(1−ρ)/ρ定律设计了这样一个指标，即λ-指标，其中80/20、90/10、70/30等都是特殊情况。λ指数对应于最大比例的来源，ρ=λ/T，产生了大约（1−ρ）的所有项目。λ的有趣的信息特性，包括其对T（总出版物）的线性依赖，使其引人注目和独特，被证明并与其他主要基本指标进行了比较。在多个数据集上，λ-指标对80/20规律的符合性优于所有基本h型指标和0.2T指标。还讨论了在使用ρ的领域中决定支配特定定律（如70/30或80/20）的可能性。通过对引用重新分配的作者简介（模拟法律变化）的敏感性分析，发现λ比其他指标更能保持法律-指标的合规性，这表明即使在具有高波动性的简介中使用它也很重要。

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引用次数: 0