Journal of Informetrics最新文献_第10页

Sequential citation counts prediction enhanced by dynamic contents 动态内容增强的顺序引文计数预测

IF 3.4 2区管理学

Journal of Informetrics Pub Date : 2025-05-01 Epub Date: 2025-02-13 DOI: 10.1016/j.joi.2025.101645

Guoxiu He , Sichen Gu , Zhikai Xue , Yufeng Duan , Xiaomin Zhu

{"title":"Sequential citation counts prediction enhanced by dynamic contents","authors":"Guoxiu He , Sichen Gu , Zhikai Xue , Yufeng Duan , Xiaomin Zhu","doi":"10.1016/j.joi.2025.101645","DOIUrl":"10.1016/j.joi.2025.101645","url":null,"abstract":"<div><div>The assessment of the impact of scholarly publications has garnered significant attention among researchers, particularly in predicting the future sequence of citation counts. However, current studies predominantly regard academic papers as static entities, failing to acknowledge the dynamic nature of their fixed content, which can undergo shifts in focus over time. To this end, we implement dynamic representations of the content to mirror chronological changes within the given paper, facilitating the sequential prediction of citation counts. Specifically, we propose a novel deep neural network called <strong>D</strong>ynam<strong>I</strong>c <strong>C</strong>ontent-aware <strong>T</strong>r<strong>A</strong>nsformer (DICTA). The proposed model incorporates a dynamic content module that leverages the power of a sequential module to effectively capture the evolving focus information within each paper. To account for dependencies between the historical and future citation counts, our model utilizes a transformer-based framework as the backbone. With the encoder-decoder structure, it can effectively handle previous citation accumulations and then predict future citation potentials. Extensive experiments conducted on two scientific datasets demonstrate that DICTA achieves impressive performance and outperforms all baseline approaches. Further analyses underscore the significance of the dynamic content module. The code is available: <span><span>https://github.com/ECNU-Text-Computing/DICTA</span><svg><path></path></svg></span></div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101645"},"PeriodicalIF":3.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143395914","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distinguishing articles in questionable and non-questionable psychology journals using quantitative indicators associated with quality 使用与质量相关的定量指标来区分可疑和非可疑心理学期刊上的文章

IF 3.4 2区管理学

Journal of Informetrics Pub Date : 2025-05-01 Epub Date: 2025-01-23 DOI: 10.1016/j.joi.2025.101640

Dimity Stephen

{"title":"Distinguishing articles in questionable and non-questionable psychology journals using quantitative indicators associated with quality","authors":"Dimity Stephen","doi":"10.1016/j.joi.2025.101640","DOIUrl":"10.1016/j.joi.2025.101640","url":null,"abstract":"<div><div>This study investigates the viability of distinguishing articles in questionable journals (QJs) from those in non-QJs on the basis of quantitative indicators typically associated with quality. Subsequently, I examine what can be deduced about the quality of articles in QJs based on the differences observed. The samples comprise 1,714 articles from 31 QJs, 1,691 articles from 16 journals indexed in Web of Science (WoS), and 1,900 articles from 45 mid-tier journals, all in the field of psychology. I contrast between samples the length of abstracts and full-texts, prevalence of spelling errors, text readability, number of references and citations, the size and internationality of the author team, the documentation of ethics and informed consent statements, and the presence of statistical errors. The results suggest that QJ articles do diverge from the disciplinary standards set by peer-reviewed journals in psychology on quantitative indicators of quality that tend to reflect the effect of peer review and editorial processes. However, mid-tier and WoS journals are also affected by potential quality concerns, such as under-reporting of ethics and informed consent processes and the presence of errors in interpreting statistics. Further research is required to develop a comprehensive understanding of the quality of articles in QJs.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101640"},"PeriodicalIF":3.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A large-scale temporal analysis of scientific production across disciplines and countries 对各学科和各国科学成果的大规模时间分析

IF 3.4 2区管理学

Journal of Informetrics Pub Date : 2025-05-01 Epub Date: 2025-04-18 DOI: 10.1016/j.joi.2025.101665

Irene Finocchi , Andrea Ribichini , Marco Schaerf

{"title":"A large-scale temporal analysis of scientific production across disciplines and countries","authors":"Irene Finocchi , Andrea Ribichini , Marco Schaerf","doi":"10.1016/j.joi.2025.101665","DOIUrl":"10.1016/j.joi.2025.101665","url":null,"abstract":"<div><div>In this article, we undertake a comprehensive large-scale analysis of the evolution of scientific communities across different disciplines and countries, spanning the period 1991-2020. Our analysis uses data obtained from Scopus and involves a total of 15,756,144 authors, 74,847,508 publications, and 1,501,206,153 citations. Besides the overall research production, we investigate multiple disciplines at various levels of aggregation (namely, scientific sectors as defined by the European Research Council and Scopus research categories). The geographical focus of our analysis takes into account first the worldwide scientific production and then addresses the 19 countries that are members of the G20 group (thus excluding the EU).</div><div>Research production generally increases with time (in terms of authors, publications, and citations), both on a global scale and specifically in each country. The growth is not only in terms of raw numbers but also relative to population and gross domestic product. The gender gap appears to be narrowing, albeit at a slower pace for STEM disciplines than others. Although the United States started out as the dominant country in all research fields, its primacy has eroded constantly with the passage of time. The fastest growing emerging country, China, recently managed to overtake the United States, at least in STEM disciplines.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101665"},"PeriodicalIF":3.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844758","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Understanding knowledge growth in scientific collaboration process: Evidence from NSFC projects 理解科学合作过程中的知识增长：来自国家自然科学基金项目的证据

IF 3.4 2区管理学

Journal of Informetrics Pub Date : 2025-05-01 Epub Date: 2025-04-18 DOI: 10.1016/j.joi.2025.101664

Zhizhen Yao , Xiaoming Huang , Haochen Song , Guoyang Rong , Feicheng Ma

{"title":"Understanding knowledge growth in scientific collaboration process: Evidence from NSFC projects","authors":"Zhizhen Yao , Xiaoming Huang , Haochen Song , Guoyang Rong , Feicheng Ma","doi":"10.1016/j.joi.2025.101664","DOIUrl":"10.1016/j.joi.2025.101664","url":null,"abstract":"<div><div>Scientific collaboration has become increasingly popular due to the growing complexity of scientific tasks, especially for scientific projects supported by large funding agencies such as The National Natural Science Foundation of China (NSFC). This study focuses on modeling the network incremental elements within the scientific collaboration process of NSFC project teams to understand the intricate knowledge growth mechanisms. Four elements representing incremental knowledge were defined: Isolation, Mixed Addition, Inclusion, and Internal Correlation. Additionally, four knowledge incremental patterns and different collaboration processes were identified. The study discovered the following key findings: (1) NSFC project teams prioritize knowledge absorption and integration during collaboration, predominantly advancing knowledge through Mixed Addition approaches. (2) Teams in Management Science and Engineering (MSE) discipline tend to expand through Mixed Addition approaches, while Economic Science (ES) teams prefer Inclusion and Internal Correlation approaches for team development compared to MSE teams. (3) The knowledge pioneering pattern negatively impacts productivity, while the emergence of knowledge expansion and enhancement patterns can lead to significant improvements. Overall, this study explores the team collaboration process from the knowledge growth perspective, which provides valuable insights for optimizing team management and improving collaboration efficiency.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101664"},"PeriodicalIF":3.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143844757","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A measure and the related models for characterizing the usage of academic journal 一种表征学术期刊使用特征的测度及相关模型

IF 3.4 2区管理学

Journal of Informetrics Pub Date : 2025-05-01 Epub Date: 2025-01-23 DOI: 10.1016/j.joi.2025.101643

Lili Qiao , Star X. Zhao , Yutong Ji , Wu Li

{"title":"A measure and the related models for characterizing the usage of academic journal","authors":"Lili Qiao , Star X. Zhao , Yutong Ji , Wu Li","doi":"10.1016/j.joi.2025.101643","DOIUrl":"10.1016/j.joi.2025.101643","url":null,"abstract":"<div><div>Based on the underlying usage data given by the <em>Web of Science</em>, we establish a novel metric, termed U<sub>h</sub>-index for multi-dimensional assessment of academic journals. Our research objectively examines the empirical and theoretical dimensions of the U<sub>h</sub>-index, assessing its validity and potential use in scientific evaluation. For this study, we conducted a quantitative analysis of the U<sub>h</sub>-index for 1,603 journals across the fields of physics, chemistry, economics, and management, and explored potential theory models. It reveals that the U<sub>h</sub>-index, as a literature metric based on usage data, is more sensitive and discriminatory compared to the h-index, which relies solely on citation data. Additionally, the U<sub>h</sub>-index and paper usage data were consistent with both the Glänzel–Schubert and the power-law model. It indicates that the U<sub>h</sub> index, as an impact observatory index, aligns with the fundamental principles of scientific knowledge dissemination, thereby holding significant scientific value. It facilitates the quantification of dissemination characteristics of core articles in journals, laying the foundation for a novel approach to categorizing and evaluating journals based on both theoretical orientation and practical application. Finally, from a multidimensional research evaluation perspective, the U<sub>h</sub> index offers a transitional dimension for observation, bridging the gap between academic citations and the broader dissemination of research through on social media.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101643"},"PeriodicalIF":3.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161177","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Journal of Informetrics Pub Date : 2025-05-01 Epub Date: 2025-04-03 DOI: 10.1016/j.joi.2025.101661

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

{"title":"Annotating scientific uncertainty: A comprehensive model using linguistic patterns and comparison with existing approaches","authors":"Panggih Kusuma Ningrum , Philipp Mayr , Nina Smirnova , Iana Atanassova","doi":"10.1016/j.joi.2025.101661","DOIUrl":"10.1016/j.joi.2025.101661","url":null,"abstract":"<div><div>We present UnScientify,<span><span><sup>1</sup></span></span> 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.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101661"},"PeriodicalIF":3.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143759742","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Acknowledging the new invisible colleague: Addressing the recognition of Open AI contributions in in scientific publishing 承认新的隐形同事：解决在科学出版中对开放人工智能贡献的承认问题

IF 3.4 2区管理学

Journal of Informetrics Pub Date : 2025-05-01 Epub Date: 2025-02-03 DOI: 10.1016/j.joi.2025.101642

Juan Gorraiz

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

Journal of Informetrics Pub Date : 2025-05-01 Epub Date: 2025-04-01 DOI: 10.1016/j.joi.2025.101658

Giovanni Abramo , Ciriaco Andrea D'Angelo

{"title":"Hyperprolific authorship: Unveiling the extent of extreme publishing in the ‘publish or perish’ era","authors":"Giovanni Abramo , Ciriaco Andrea D'Angelo","doi":"10.1016/j.joi.2025.101658","DOIUrl":"10.1016/j.joi.2025.101658","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101658"},"PeriodicalIF":3.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

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

Journal of Informetrics Pub Date : 2025-05-01 Epub Date: 2025-03-22 DOI: 10.1016/j.joi.2025.101657

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

引用次数: 0

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

Journal of Informetrics Pub Date : 2025-05-01 Epub Date: 2025-04-05 DOI: 10.1016/j.joi.2025.101662

Chengzhi Zhang, Jiaqi Zeng, Yi Zhao

{"title":"Is higher team gender diversity correlated with better scientific impact?","authors":"Chengzhi Zhang, Jiaqi Zeng, Yi Zhao","doi":"10.1016/j.joi.2025.101662","DOIUrl":"10.1016/j.joi.2025.101662","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101662"},"PeriodicalIF":3.4,"publicationDate":"2025-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0