Journal of Informetrics最新文献

{"title":"Integrating persistence process into the analysis of technology convergence using STERGM","authors":"Guancan Yang ,&nbsp;Di Liu ,&nbsp;Ling Chen ,&nbsp;Kun Lu","doi":"10.1016/j.joi.2024.101632","DOIUrl":"10.1016/j.joi.2024.101632","url":null,"abstract":"<div><div>Understanding the dynamics of technology convergence is indispensable for both academic and industrial perspectives. Traditional analyses have mainly focused on the link formation process, overlooking the role that persistence process plays in shaping technology networks. This paper endeavors to fill this gap by incorporating the persistence process into the analysis of technology convergence using the <em>Separate Temporal Exponential Random Graph Model</em> (STERGM). Utilizing a decade-long dataset of breast cancer drug patents, we provide a comprehensive view of technology convergence mechanisms and their predictive capabilities. Our findings reveal significant differences in network effects between formation and persistence processes, indicating that focusing on only one may misrepresent the evolution of technology networks. The combined model achieves an F1 score of 69.54% in empirical forecasting, confirming its practical utility. Additionally, we introduce Intensification Networks to examine how existing ties strengthen or weaken over time, uncovering the critical role of intensification in the long-term evolution of technology convergence. By capturing both the formation of new ties and the intensification of existing ones, our model offers a more nuanced and forward-looking understanding of convergence dynamics, particularly in identifying potential areas for future technology convergence.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101632"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165369","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}

{"title":"Evolution and effect of meeting abstracts in JCR journals","authors":"David Melero-Fuentes ,&nbsp;Remedios Aguilar-Moya ,&nbsp;Juan-Carlos Valderrama-Zurián ,&nbsp;Juan Gorraiz","doi":"10.1016/j.joi.2024.101631","DOIUrl":"10.1016/j.joi.2024.101631","url":null,"abstract":"<div><div>The purpose of the present study is to analyse the presence and evolution in the last 13 years of the document type “Meeting Abstract” in the database where they are best represented, i.e. in the Web of Science Core Collection. We have also studied in which categories and in which type of journals they have a significant presence.</div><div>Frequency analyses of meeting abstracts (absolute and ratios) were performed on years, indexes, categories and topics variables, and the Impact Factor was calculated without the citations obtained by the meeting abstracts.</div><div>The results obtained show that in disciplines such as <em>Clinical Medicine, Neuroscience &amp; Behavior.</em> and <em>Biology &amp; Biochemistry</em>, they play a very important role due to both their number and the number of attracted citations, and that they are regularly published in top journals, including Q1 according to the Journal of Citation Reports. Our results also corroborate the hypothesis that they inflate the Impact Factor and therefore are one of the reasons for the high absolute values of this indicator in categories like <em>Oncology</em> and <em>Hematology.</em></div><div>This study is of great relevance for researchers and policymakers, because it helps to identify in which disciplines Meeting Abstracts have relevance and they should be considered for the calculation of indicators in bibliometric practices, and opens the door to research into their relationship with other documentary typologies within the social processes of scientific communication in different sciences.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101631"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165422","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}

{"title":"Corrigendum to “A framework armed with node dynamics for predicting technology convergence” [Journal of Informetrics 18 (2024) 101583]","authors":"Guancan Yang ,&nbsp;Jiaxin Xing ,&nbsp;Shuo Xu ,&nbsp;Yuntian Zhao","doi":"10.1016/j.joi.2024.101629","DOIUrl":"10.1016/j.joi.2024.101629","url":null,"abstract":"","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101629"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165449","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}

{"title":"A Dirichlet-Multinomial mixture model of Statistical Science: Mapping the shift of a paradigm","authors":"Massimo Bilancia ,&nbsp;Rade Dačević","doi":"10.1016/j.joi.2024.101633","DOIUrl":"10.1016/j.joi.2024.101633","url":null,"abstract":"<div><div>Using Bayesian natural language processing (NLP) methods and a scalable variational algorithm tailored for mixtures of discrete positive data, we analyzed a large corpus of 111,411 eprints submitted to the arXiv repository between 1994 and 2022 in the Statistics category (the primary classification for these eprints on arXiv). Our objective is to assess the impact of Machine Learning (ML) on the field of Statistics–specifically, to determine whether the introduction of ML has led to a fundamental paradigm shift, transforming traditional statistical problems or creating entirely new ones, or if this perceived revolution is primarily occurring outside the field of Statistics. Our findings suggest that the only significant paradigm shift for Statistics as a scientific discipline remains the Bayesian revolution that began in the early 1990s.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101633"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165371","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}

{"title":"Investigating the research output of institutions","authors":"Wolfgang G. Stock ,&nbsp;Gerhard Reichmann ,&nbsp;Christian Schlögl","doi":"10.1016/j.joi.2025.101638","DOIUrl":"10.1016/j.joi.2025.101638","url":null,"abstract":"<div><div>Describing, analyzing, and evaluating research institutions are among the main tasks of scientometrics and research evaluation. But how can we optimally search for an institution's research output? Possible search arguments include institution names, affiliations, addresses, and affiliated authors’ names. Prerequisites of these search tasks are complete lists (or at least good approximations) of the institutions’ publications, and—in later steps—their citations, and topics. When searching for the publications of research institutions in an information service, there are two options, namely (1) searching directly for the name of the institution and (2) searching for all authors affiliated with the institution in a defined time interval. Which strategy is more effective? More specifically, do informetric indicators such as recall and precision, search recall and search precision, and relative visibility change depending on the search strategy? What are the reasons for differences? To illustrate our approach, we conducted an illustrative study on two information science institutions and identified all staff members. The search was performed using the Web of Science Core Collection (WoS CC). As a performance indicator, applying fractional counting and considering co-affiliations of authors, we used the institution's relative visibility in an information service. We also calculated two variants of recall and precision at the institution level, namely search recall and search precision as informetric measures of performance differences between different search strategies (here: author search versus institution search) on the same information service (here: WoS CC) and recall and precision in relation to the complete set of an institution's publications. For all our calculations, there is a clear result: Searches for affiliated authors outperform searches for institutions in WoS. However, especially for large institutions it is difficult to determine all the staff members in the time interval of research. Additionally, information services (including WoS) are incomplete and there are variants for the names of institutions in the services. Therefore, searching for institutions and the publication-based quantitative evaluation of institutions are very critical issues.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101638"},"PeriodicalIF":3.4,"publicationDate":"2025-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161104","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}

{"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":"2025-01-23","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}

{"title":"A measure and the related models for characterizing the usage of academic journal","authors":"Lili Qiao ,&nbsp;Star X. Zhao ,&nbsp;Yutong Ji ,&nbsp;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_h-index for multi-dimensional assessment of academic journals. Our research objectively examines the empirical and theoretical dimensions of the U_h-index, assessing its validity and potential use in scientific evaluation. For this study, we conducted a quantitative analysis of the U_h-index for 1,603 journals across the fields of physics, chemistry, economics, and management, and explored potential theory models. It reveals that the U_h-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_h-index and paper usage data were consistent with both the Glänzel–Schubert and the power-law model. It indicates that the U_h 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_h 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":"2025-01-23","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}

{"title":"Forecasting topic trends of blockchain utilizing topic modeling and deep learning-based time-series prediction on different document types","authors":"Yejin Park ,&nbsp;Seonkyu Lim ,&nbsp;Changdai Gu ,&nbsp;Arida Ferti Syafiandini ,&nbsp;Min Song","doi":"10.1016/j.joi.2025.101639","DOIUrl":"10.1016/j.joi.2025.101639","url":null,"abstract":"<div><div>Topic trends in rapidly evolving domains like blockchain are dynamic and pose prediction challenges. To address this, we propose a novel framework that integrates topic modeling, clustering, and time-series deep learning models. These models include both non-graph-based and graph-based approaches. Blockchain-related documents of three types—academic papers, patents, and news articles—are collected and preprocessed. Random and topic subgraphs are constructed as inputs for model training and forecasting across various time epochs. The four models (LSTM, GRU, AGCRN, and A3T-GCN) are trained on random subgraphs, and the trained models forecast topic trends using topic subgraphs. We also analyze the distinctive characteristics of each document type and investigate the causal relationships between them. The results indicate that non-graph-based models, such as LSTM, perform better on periodic data like academic papers, whereas graph-based models, such as AGCRN and A3T-GCN, excel at capturing non-periodic patterns in patents and news articles. Our framework demonstrates robust performance, offering a versatile tool for blockchain-related trend analysis and forecasting. The code and environments are available at <span><span>https://github.com/textmining-org/topic-forecasting</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101639"},"PeriodicalIF":3.4,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161175","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}

{"title":"Two separated worlds: On the preference of influence in life science and biomedical research","authors":"Zuguang Gu","doi":"10.1016/j.joi.2025.101641","DOIUrl":"10.1016/j.joi.2025.101641","url":null,"abstract":"<div><div>We introduced a new metric, “citation enrichment”, to measure country-to-country influence using citation data. This metric evaluates the degree to which a country prefers to cite another country compared to a random citation process. We applied the citation enrichment method to over 12 million publications in the life science and biomedical fields and we have the following key findings: 1) The global scientific landscape is divided into two separated worlds where developed Western countries exhibit an overall mutual under-influence with the rest of the world; 2) Within each world, countries form clusters based on their mutual citation preferences, with these groupings strongly associated with their geographical and cultural proximity; 3) The two worlds exhibit distinct patterns of the influence balance among countries, revealing underlying mechanisms that drive influence dynamics. We have constructed a comprehensive world map of scientific influence which greatly enhances the deep understanding of the international exchange of scientific knowledge. The citation enrichment metric is developed under a well-defined statistical framework and has the potential to be extended into a versatile and powerful tool for bibliometrics and related research fields.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101641"},"PeriodicalIF":3.4,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161178","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}

{"title":"A comprehensive comparative analysis of publication monopoly phenomenon in scientific journals","authors":"Chengjun Zhang ,&nbsp;ZhengJu Ren ,&nbsp;Gaofeng Xiang ,&nbsp;Wenbin Yu ,&nbsp;Zeyu Xu ,&nbsp;Jin Liu ,&nbsp;Yadang Chen","doi":"10.1016/j.joi.2024.101628","DOIUrl":"10.1016/j.joi.2024.101628","url":null,"abstract":"<div><div>The increasing number of academic practitioners has resulted in a significantly increased volume of scientific papers, attracting considerable interest among researchers examining this correlation. However, little research has been devoted to the phenomenon of scientists monopolizing authorship in academic journals. This study thus introduces the term Publication Monopoly (PM) to describe this effect. The study refers to the prolific authors as Monopoly Authors. In addition, it proposes a Monopoly Index to assess PM severity. For each journal, the Monopoly Contribution (MC) quantifies the impact of Monopoly Authors. Using the Open Academic Graph dataset, our analysis explores the prevalence of PM and the corresponding MC in selected journals and academic fields. The findings demonstrate a positive relationship between the number of articles published and the likelihood of PM occurrence in most journals. Furthermore, fields relying heavily on laboratory environments or specialized equipment are particularly susceptible to PM. Additionally, once a journal becomes entrenched in PM, it is challenging to alleviate this phenomenon over time. Our study of PM aimed to prompt academic practitioners to carefully consider the likelihood of acceptance in journals characterized by high PM levels. Moreover, the study encourages journals to reconsider their need to accept more articles from Monopoly Authors.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101628"},"PeriodicalIF":3.4,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142759638","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}