Journal of Informetrics最新文献

{"title":"A multi-entity reinforced main path analysis: Heterogeneous network embedding considering knowledge proximity","authors":"Zhaoping Yan ,&nbsp;Kaiyu Fan","doi":"10.1016/j.joi.2024.101593","DOIUrl":"10.1016/j.joi.2024.101593","url":null,"abstract":"<div><div>Main path analysis (MPA) is an important approach in detecting the trajectory of knowledge diffusion in a specific research domain. Previous studies always focus on citation-based relationships, overlooking other structural forms in citation network. This study introduces a multi-entity reinforced MPA model by constructing a knowledge graph from paper metadata, including citations, authors, journals, and keywords. We construct heterogeneous network to reveal relationships among various entities. Different knowledge graph embedding models are employed to train the network, thereby obtaining entity and relation embeddings. The cosine similarity algorithm is adopted to measure the knowledge proximity between these embeddings. We take the Internet of Thing domain as an example to verify the performance of the multi-entity reinforced MPA through both quantitative and qualitative analysis. Our findings indicate that the adjusted MPA exhibits stronger topic relevance, demonstrating the effectiveness of the method in capturing complex knowledge relationships.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101593"},"PeriodicalIF":3.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327622","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":"Effects of research funding on the academic impact and societal visibility of scientific research","authors":"Guiyan Ou ,&nbsp;Kang Zhao ,&nbsp;Renxian Zuo ,&nbsp;Jiang Wu","doi":"10.1016/j.joi.2024.101592","DOIUrl":"10.1016/j.joi.2024.101592","url":null,"abstract":"<div><div>Assessing the effectiveness of research funding is of significant value to policymakers. Previous studies have mainly concentrated on the academic impact of funded research, yet the exploration of how research funding affects the societal visibility of research has been significantly lacking. Thus, this study addresses this gap by examining papers published by Chinese scholars and compares the effects of funding on papers’ societal visibility (measured by Altmetric Attention Scores) with those for papers' academic impact (measured by citation counts). This study reveals several interesting findings: First, research supported by funding demonstrates a lower societal visibility, albeit a higher academic impact, compared to those without funding. Second, the societal visibility of research supported by small to moderate number of funding sources is still lower than those without research funding. In contrast, a paper's academic impact is higher if it has a higher number of research funding sources. Third, the effects of funding on papers’ academic impact and societal visibility differ by funding mechanisms—having industry funding significantly increases the societal visibility of research. These findings can aid research policymakers’ funding allocation decisions and inform better assessment of research outcomes.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101592"},"PeriodicalIF":3.4,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142327484","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":"On the temporal diversity of knowledge in science","authors":"Alex J. Yang","doi":"10.1016/j.joi.2024.101594","DOIUrl":"10.1016/j.joi.2024.101594","url":null,"abstract":"<div><div>Understanding the diversity of scientific knowledge is pivotal for elucidating trends in science and innovation. While interdisciplinarity and team diversity have been extensively studied, the temporal diversity of knowledge remains underexplored. This paper introduces a novel framework for assessing temporal diversity in scholarly research. Analyzing 31 million articles from the past seven decades, I revealed an increasing trend in temporal diversity, reflecting the cumulative nature of scientific knowledge. Additionally, I found that temporal diversity is negatively associated with citation impact but positively associated with disruption. These patterns are robust and consistent across different contexts. Moreover, the findings suggest that higher temporal diversity leading to greater disruption may be primarily due to the use of older references. However, the disadvantages of temporal diversity in terms of citation impact cannot be entirely explained by other factors. Collectively, this study elucidates the dynamics of temporal diversity and its implications for innovation, providing new frameworks in the science of science and evidence on how innovation is driven by the temporal diversity of knowledge.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101594"},"PeriodicalIF":3.4,"publicationDate":"2024-09-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142319662","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":"Article ranking with location-based weight in contextual citation network","authors":"Jong Hee Jeon,&nbsp;Jason J. Jung","doi":"10.1016/j.joi.2024.101591","DOIUrl":"10.1016/j.joi.2024.101591","url":null,"abstract":"<div><p>This paper proposes a method to evaluate academic impact that focuses on spatial context in which citations occur in sections of citing papers. Previous studies measured impact of papers using external factors such as journals, time, and authors. However, these methods overlooks context of citations, leading to problem of treating papers with same citation counts equivalently. To overcome this issue, we designed a citation network by reflecting on the spatial context in which cited papers are cited in the citing paper and measured their impact. Spatial context is defined by the specific section of the citing paper (Introduction, Method, Result, Discussion, Conclusion) where the citation appears. We collected 818 citing papers and 13,257 cited papers from 2013–2022 from Journal of Informetrics and constructed a context-reflected citation network. Further, we utilized CRITIC method and weighted PageRank algorithm for measuring section-specific weights and impact. Results obtained in this study suggest that the impact of cited papers varies significantly depending on the section context in which they appear. We use Kendall <em>τ</em> coefficient for analyzing correlation between “times cited” rankings and contextual PageRank. The Kendall <em>τ</em> coefficient between two ranks for entire dataset is 0.473. This study provides a multidimensional framework to assess the impact of academic papers, suggesting that future evaluations should consider not only the number of citations but also their context.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101591"},"PeriodicalIF":3.4,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241102","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":"Do conference-journal articles receive more citations? A case study in physics","authors":"Dengsheng Wu ,&nbsp;Shuwen Wang ,&nbsp;Weixuan Xu ,&nbsp;Jianping Li","doi":"10.1016/j.joi.2024.101590","DOIUrl":"10.1016/j.joi.2024.101590","url":null,"abstract":"<div><p>Conference-journal articles, which are expanded versions of conference proceedings papers, play an essential role in disseminating scientific knowledge but remain understudied. In the context of increasingly stringent research evaluation systems, this study focuses on conference-journal articles, examining the effectiveness of journals in selecting conference-derived publications. We also explore the factors influencing the citations of conference-journal articles. Here, we focused on Physics, analyzing 59,329 conference-journal articles published between 2012 and 2020, matched with general journal articles and conference proceedings papers based on the conference and journal. Results show that conference-journal articles receive significantly more citations than conference proceedings papers but fewer than general journal articles. Conference-journal articles in special issues receive fewer citations than those in regular issues. A U-shaped pattern emerges between the duration from the conference convening to the journal publication and the citation. We also found that conferences with sponsorship and those held in OECD member countries are more likely to produce highly cited conference-journal articles. Additionally, results indicate that conferences held in the USA, Japan, France, China, and Poland produce the most conference-journal articles, with articles from conferences in the USA, Japan, and France receiving relatively high citation counts. In contrast, articles from conferences held in China and Poland receive relatively low citation counts. This research provides valuable insights for academic conference committees, journal managers, and conference participants.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101590"},"PeriodicalIF":3.4,"publicationDate":"2024-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142228543","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":"An effective framework for measuring the novelty of scientific articles through integrated topic modeling and cloud model","authors":"Zhongyi Wang ,&nbsp;Haoxuan Zhang ,&nbsp;Jiangping Chen ,&nbsp;Haihua Chen","doi":"10.1016/j.joi.2024.101587","DOIUrl":"10.1016/j.joi.2024.101587","url":null,"abstract":"<div><p>Novelty is a critical characteristic of innovative scientific articles, and accurately identifying novelty can facilitate the early detection of scientific breakthroughs. However, existing methods for measuring novelty have two main limitations: (1) Metadata-based approaches, such as citation analysis, are retrospective and do not alleviate the pressures of the peer review process or enable timely tracking of scientific progress; (2) Content-based methods have not adequately addressed the inherent uncertainty between the qualitative concept of novelty and the textual representation of papers. To address these issues, we propose a practical and effective framework for <strong>m</strong>easuring the <strong>n</strong>ovelty of <strong>s</strong>cientific <strong>a</strong>rticles through <strong>i</strong>ntegrated <strong>t</strong>opic <strong>m</strong>odeling and <strong>c</strong>loud <strong>m</strong>odel, referred to as <strong>MNSA-ITMCM</strong>. In this framework, papers are represented as topic combinations, and novelty is reflected in the organic reorganization of these topics. We use the BERTopic model to generate semantically informed topics, and then apply a topic selection algorithm based on maximum marginal relevance to obtain a topic combination that balances similarity and diversity. Furthermore, we leverage the cloud model from fuzzy mathematics to quantify novelty, overcoming the uncertainty inherent in natural language expression and topic modeling to improve the accuracy of novelty measurement. To validate the effectiveness of our framework, we conducted empirical evaluations on papers from the Cell 2021 journal (biomedical domain) and the ICLR 2023 conference (computer science domain). Through correlation analysis and prediction error analysis, our framework demonstrated the ability to identify different types of novel papers and accurately predict their novelty levels. The proposed framework is applicable across diverse scientific disciplines and publication venues, benefiting researchers, librarians, science evaluation agencies, policymakers, and funding organizations by improving the efficiency and comprehensiveness of identifying novelty research.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101587"},"PeriodicalIF":3.4,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142171980","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":"Exploring the potential of disruptive innovation in the social sciences: A quantitative study of its impact on societal visibility","authors":"Yingqun Li ,&nbsp;Ningyuan Song ,&nbsp;Yu Shen ,&nbsp;Lei Pei","doi":"10.1016/j.joi.2024.101584","DOIUrl":"10.1016/j.joi.2024.101584","url":null,"abstract":"<div><p>Scientific innovation serves as the driving force behind societal progress. In contrast to conservative innovation, disruptive innovation reshapes scientific paradigms and trajectories, significantly influencing both the scientific community and societal development. This study employs an extensive empirical dataset to explore the potential of disruptive innovation to enhance the societal visibility of scientific research. Our research reveals that disruptive innovation significantly enhances societal visibility, increasing it by 11.96% compared to consolidating innovation. Furthermore, disruptive innovation does not directly lead to early-stage \"breakthroughs\" in scientific endeavors, but it does have a notable \"acceleration\" effect on societal visibility. Particularly striking is its ability to promote visibility of scientific research on social media platforms such as Twitter and blogs. However, its influence is insignificant in news articles and policy documents. This phenomenon may be attributed to the high-risk nature of disruptive innovation, which conflicts with the high level of trust, professionalism, and certainty sought in news and policy. This study carries essential implications for selecting innovative directions, the channels through which innovation is disseminated, and the formulation of science policies.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101584"},"PeriodicalIF":3.4,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142157529","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":"How does policy information shape its adoption? A citation analysis of large-scale energy policies in China","authors":"Leilei Liu ,&nbsp;Zhichao Ba ,&nbsp;Lei Pei","doi":"10.1016/j.joi.2024.101589","DOIUrl":"10.1016/j.joi.2024.101589","url":null,"abstract":"<div><p>Understanding the antecedents of policy adoption is essential for facilitating policy diffusion and designing follow-up policies. Previous research on drivers of policy adoption primarily focused on local attributes and government interactions, often neglecting the influence of the policy information itself. This study systematically investigates how policy information (policy design, topics, and attributes) shapes its adoption. Drawing on the Elaboration Likelihood Model (ELM), we developed a framework to explain how such policy information embedded in policy documents influences policy adoption through central and peripheral routes. The adoption performance of each policy is quantified based on a novel policy citation approach. An empirical analysis of large-scale energy policies in China demonstrates that differentiated policy designs and topics exert heterogeneous effects on the intensity and speed of policy adoption. Moreover, their impact on subsequent policy adoptions is more pronounced than on first-time policy adoptions. Policy attributes such as institutional collaboration, reasonable timing agendas, and referencing high-impact policies positively influence policy adoption performance. Additionally, the validity level of a policy positively moderates the relationship between content information and adoption performance. Our research provides practical implications for policymakers to strategically craft appropriate policy-making and targeted promotion strategies for effective policy diffusion.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101589"},"PeriodicalIF":3.4,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142157528","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":"Exploring team creativity: The nexus between freshness and experience","authors":"Wenlong Yang,&nbsp;Yang Wang","doi":"10.1016/j.joi.2024.101588","DOIUrl":"10.1016/j.joi.2024.101588","url":null,"abstract":"<div><p>Scientific collaborations are widely acknowledged as a key factor in advancing contemporary science. Using the absence of prior collaboration relationships among all team members of a focal paper to assess team freshness, previous studies demonstrated strong associations between team freshness and the ability to generate original and multidisciplinary papers. However, the intricate interplay between team freshness and the experiences of new members remains less explored. For example, individuals can be classified based on their experiences or ages, distinguishing between newcomers with limited experience and incumbents with a track record of publications. Using the existing definition of team freshness, we focus on categorizing fresh teams into two distinct types: those consisting of fresh incumbents and those with fresh newcomers. Utilizing a comprehensive dataset comprising over 5 million articles, we systematically investigate the relationship between team freshness, the freshness of incumbents, and the freshness of newcomers on various creativity measurements. Our analysis yields several key findings. Firstly, both team freshness and the freshness of newcomers display a declining trend over time, whereas the freshness of incumbents remains stable. Secondly, we observe strong positive associations between team freshness, the freshness of incumbents, and the freshness of newcomers with regard to various creativity indicators. Strikingly, we emphasize substantial promotional powers of the freshness of incumbents on creativity, even after adjusting for overall team freshness. Our results have important policy implications related to the formation of creative teams.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101588"},"PeriodicalIF":3.4,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142151124","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":"Profiling team exploration strategies of collaborating authors from artificial intelligence in computer science","authors":"Adarsh Raghuvanshi ,&nbsp;Vinayak","doi":"10.1016/j.joi.2024.101586","DOIUrl":"10.1016/j.joi.2024.101586","url":null,"abstract":"<div><p>To identify collaboration trends with coauthors, this paper elaborates a theoretical framework by introducing a measure to quantify exploration of the author in joining teams of coauthors with respect to the extreme exploration possibilities. Using the clustering coefficient, we gauge the team exploration from the author-centric vista evaluating configuration values of the ego networks. This value is normalized with respect to the maximum exploration possibilities for the author facilitating us to derive a measure, viz., the team exploration score for the team exploration strategy. We further derive a dynamical version of this measure. The average profiles of the exploration strategies are compared for the authors from the USA, England, and India publishing in a rapidly growing and collaboration-extensive field, viz. artificial intelligence in computer science, in the time window from 1990 to 2020. The bibliometric data are sourced from the <em>Clarivate</em> Web of Science. Configuration values are evaluated in the ascending year of publications in year-long time windows to compute the team exploration score for each author. Our analysis shows that the annually averaged profiles of authors corresponding to the three countries are almost constantly increasing toward high team exploration scores. Also, in the career-averaged profiles, authors publishing more than 20 papers have mostly adopted exploratory strategies.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 4","pages":"Article 101586"},"PeriodicalIF":3.4,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142136382","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}