Journal of Informetrics最新文献

{"title":"Trends in emerging topics generation across countries in life science and medicine","authors":"Bryan Mathis ,&nbsp;Ryosuke L. Ohniwa","doi":"10.1016/j.joi.2024.101552","DOIUrl":"https://doi.org/10.1016/j.joi.2024.101552","url":null,"abstract":"<div><p>While advantages of global homogenization and standardization have been discussed on scientific and technological research activities, specific discussion on the disadvantages to generate scientific innovation has been limited. In this study, we aim to clarify the impact of globalization to generate emerging topics in life science and medicine by applying the emerging keywords (EKs) and highly successful emerging keywords (HS-EKs) methodology, which represent scientometric elements of emerging topics and high-impact emerging topics, respectively, as indicators. We analyzed all paper output from 53 countries using PubMed and found a global increase in paper output and EK generation in line with economic growth in the past 50 years. However, the efficiency to generate scientific innovation, reflected in HS-EKs, was significantly reduced and this effect was independent of country-level economic output. We also reported homogenization in research topics over the study period as a possible factor for the observed decrease in HS-EK generation efficiency. Finally, we discuss the foundational issues that gave rise to homogenized science, the impact on scientific innovation, and what policies might be necessary to repair the scientific innovation-generating engine.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 3","pages":"Article 101552"},"PeriodicalIF":3.7,"publicationDate":"2024-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141263985","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":"Foundations of Narrative Bibliometrics","authors":"Daniel Torres-Salinas ,&nbsp;Enrique Orduña-Malea ,&nbsp;Ángel Delgado-Vázquez ,&nbsp;Juan Gorraiz ,&nbsp;Wenceslao Arroyo-Machado","doi":"10.1016/j.joi.2024.101546","DOIUrl":"10.1016/j.joi.2024.101546","url":null,"abstract":"<div><p>The document 'Foundations of Narrative Bibliometrics' provides an analysis of the evolution of scientific assessment, highlighting the influence of manifestos such as DORA and CoARA in shaping ethical and responsible practices in academia, as well as their assimilation by Spanish scientific policies. It connects this context with the contributions of evaluative bibliometrics, emphasising the transition towards a more integrative approach that advocates for a balance between quantitative and qualitative methods in research evaluation. Furthermore, it underscores how the Narrative Curriculum has emerged as one of the fundamental tools in new evaluation processes, as it allows for the description of the complexity and context of academic achievements. Narrative Bibliometrics is proposed, defined as the use of bibliometric indicators to generate stories that support the defence and exposition of a scientific curriculum and/or its individual contributions within the framework of a scientific evaluation process, which demands narratives. To introduce the reader, it presents, in a non-exhaustive manner, sources, indicators, and practical cases for effectively applying Narrative Bibliometrics in various scientific evaluation contexts. Hence, this document contributes to the responsible use of bibliometric indicators, serving as a tool for evaluators and researchers.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 3","pages":"Article 101546"},"PeriodicalIF":3.7,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1751157724000592/pdfft?md5=7fbc6cfc08d9d3423ead6951c148e6b5&pid=1-s2.0-S1751157724000592-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141190650","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":"The mediating impact of citation scope: Evidence from China's ESI publications","authors":"Li Tang ,&nbsp;Defang Yang ,&nbsp;Mingxing Wang ,&nbsp;Ying Guo","doi":"10.1016/j.joi.2024.101541","DOIUrl":"10.1016/j.joi.2024.101541","url":null,"abstract":"<div><p>The highly skewed nature of research influence has been widely acknowledged. Among extant studies examining contributing factors, most focus on the hard sciences in developed economies with very few examining the social sciences in emerging powers. The impact of citation scope is likewise left largely underexplored. In this paper, we develop two novel measures of citation scope using geography and research field as metrics and explore their role in boosting academic impact. Our results support geography as a citation scope serving an important pathway through which international collaboration affects academic impact. Such effect increases in prominence in later years. We do not find evidence indicating the mediating effect of research field citation scope on scholarly recognition.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 3","pages":"Article 101541"},"PeriodicalIF":3.7,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141190583","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":"Uncovering milestone papers: A network diffusion and game theory approach","authors":"Wei Zhang ,&nbsp;Juyang Cao ,&nbsp;Manuel Sebastian Mariani ,&nbsp;Zhen-Zhen Wang ,&nbsp;Mingyang Zhou ,&nbsp;Wei Chen ,&nbsp;Hao Liao","doi":"10.1016/j.joi.2024.101545","DOIUrl":"10.1016/j.joi.2024.101545","url":null,"abstract":"<div><p>Methods to rank documents in large-scale citation data are increasingly assessed in terms of their ability to identify small sets of expert-selected papers. Here, we propose an algorithm for the accurate identification of milestone papers from citation networks. The algorithm combines an influence propagation process with game theory concepts. It outperforms state-of-the-art metrics in the identification of milestone papers in aggregate citation network data, while potentially mitigating the ranking's temporal bias compared with metrics that have similar milestone identification performance. The proposed method sheds light on the interplay between ranking accuracy and temporal bias.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 3","pages":"Article 101545"},"PeriodicalIF":3.7,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141191079","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":"Are the bibliometric growth patterns of excellent scholars similar? From the analysis of ACM Fellows","authors":"Xianzhe Peng,&nbsp;Huixin Xu,&nbsp;Jin Shi","doi":"10.1016/j.joi.2024.101543","DOIUrl":"10.1016/j.joi.2024.101543","url":null,"abstract":"<div><p>The growth of excellent scholars provides paradigmatic career paths leading to research success, as their research capabilities ultimately manifest as fluctuations in bibliometric indexes. Examining the commonalities in the trajectories of these bibliometric indexes displays the universal characteristics of their growth process, and furtherly shows exemplary routes to scientific success. In this study, we examine 287 excellent scholars elected as ACM Fellows in the field of computer science from 2016s to 2020s. Based on their changes in productivity, impact, and comprehensive abilities, we categorize them into three categories, four categories, and six categories, respectively. Most of these scholars experience continuous growth in productivity during the early development stages, maintaining a prolonged period of high productivity in the mid-later maturity stages. Their impact rises smoothly and consistently, while the growth of their comprehensive abilities is relatively gradual, remaining at above-average levels in the mid-later maturity stages. Furthermore, the level of recognition within the scientific research community varies for different categories of scholars, and there are also differences in the growth patterns between scholars from Asia and those from Western regions.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 3","pages":"Article 101543"},"PeriodicalIF":3.7,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063565","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":"Transdisciplinary fine-grained citation content analysis: A multi-task learning perspective for citation aspect and sentiment classification","authors":"Ling Kong ,&nbsp;Wei Zhang ,&nbsp;Haotian Hu ,&nbsp;Zhu Liang ,&nbsp;Yonggang Han ,&nbsp;Dongbo Wang ,&nbsp;Min Song","doi":"10.1016/j.joi.2024.101542","DOIUrl":"https://doi.org/10.1016/j.joi.2024.101542","url":null,"abstract":"<div><p>The diffusion of citation knowledge is an important measure of transdisciplinary scientific impact and the diversity of transdisciplinary citation content (sentences). Moreover, combining citation sentiment (CS) and citation aspect (CA) can help researchers identify the attitudes, ideas, or positions reflected in the evolution of scientific elements (e.g., theories, techniques, and methods). This is because of their use by scholars from different disciplines, paving the way toward transdisciplinary penetration and the development of domain knowledge through the proliferation of cited knowledge. However, most studies mainly address citation aspect classification (CAC) and citation sentiment classification (CSC) separately, ignoring their shared features of interactions. In this study, we construct a dataset for transdisciplinary citation content analysis using citations and academic full texts from the Chinese Social Sciences Citation Index (CSSCI), which includes 14,832 manually-annotated citations. Thereafter, we utilized the developed dataset to conduct a transdisciplinary fine-grained citation content analysis by combining CAC and CSC. The objective of the CAC task was to classify transdisciplinary citations into theoretical concepts (TC), methodological techniques (MT), and data information (DI), whereas the CSC task classified citations into positive, negative, and neutral classes. Furthermore, we leveraged a multi-task learning (MTL) model to perform CAC and CSC jointly and then compared its performance to those of several widely-used deep learning models. Our model achieved 83.10 % accuracy for CAC and 80.46 % accuracy for CSC, demonstrating its superiority to single-task systems. This indicates the strong correlation between the CAC and CSC of transdisciplinary citation tasks, benefiting from each other when learned concurrently. This new method can be used as an auxiliary decision support system to extend the analysis of transdisciplinary citation content.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 3","pages":"Article 101542"},"PeriodicalIF":3.7,"publicationDate":"2024-05-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140918104","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":"The misuse of the nonlinear field normalization method: Nonlinear field normalization citation counts at the paper level should not be added or averaged","authors":"Xing Wang","doi":"10.1016/j.joi.2024.101531","DOIUrl":"https://doi.org/10.1016/j.joi.2024.101531","url":null,"abstract":"<div><p>Nonlinear field normalization citation counts at the paper level obtained using nonlinear field normalization methods should not be added or averaged. Unfortunately, there are many cases adding or averaging the nonlinear normalized citation counts of individual papers that can be found in the academic literature, indicating that nonlinear field normalization methods have long been misused in academia. In this paper, we performed the following three research works. First, we analyzed why the nonlinear normalized citation counts of individual papers should not be added or averaged from the perspective of theoretical analysis in mathematics: we provided mathematical proofs for the crucial steps of the analysis. Second, we systematically classified the existing main field normalization methods into linear and nonlinear field normalization methods. Third, we used real citation data to explore the error effects caused by adding or averaging the nonlinear normalized citation counts on practical research evaluation results. The above three research works provide a theoretical basis for the proper use of field normalization methods in the future. Furthermore, because our mathematical proof is applicable to all nonlinear data in the entire real number domain, our research works are also meaningful for the whole field of data and information science.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 3","pages":"Article 101531"},"PeriodicalIF":3.7,"publicationDate":"2024-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1751157724000440/pdfft?md5=2cee2433e7c0ada2b3ebaae74c0e5282&pid=1-s2.0-S1751157724000440-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140878581","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":"Open peer review correlates with altmetrics but not with citations: Evidence from Nature Communications and PLoS One","authors":"Xi Cheng ,&nbsp;Haoran Wang ,&nbsp;Li Tang ,&nbsp;Weiyan Jiang ,&nbsp;Maotian Zhou ,&nbsp;Guoyan Wang","doi":"10.1016/j.joi.2024.101540","DOIUrl":"https://doi.org/10.1016/j.joi.2024.101540","url":null,"abstract":"<div><p>Against the backdrop of increasing transparency in scientific publications and the complexity of citation motivations, the applicability and efficacy of open peer review (OPR) remain controversial. Utilizing a dataset of citations and altmetrics for all articles published in <em>Nature Communications</em> and <em>PloS One</em>, in this study the impact of OPR is investigated from the dimensions of open review reports and open identity reviewers. The analysis reveals articles subjected to OPR have no obvious advantage in citations but a notable higher score in altmetrics. The distribution of data variation across most disciplines, displaying a statistically significant difference between OPR and non-OPR, mirrors the overall trend. Two potential explanations for the disparity in OPR's impact on citations compared to altmetrics are proposed. The first relates to the quality heterogeneity between OPR and non-OPR research, while the second is related to the diverse authors citing and mentioning articles in distinct communities. This study's findings carry policy implications for future OPR practices.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 3","pages":"Article 101540"},"PeriodicalIF":3.7,"publicationDate":"2024-04-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140807259","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":"Societal and scientific impact of policy research: A large-scale empirical study of some explanatory factors using Altmetric and Overton","authors":"Pablo Dorta-González ,&nbsp;Alejandro Rodríguez-Caro ,&nbsp;María Isabel Dorta-González","doi":"10.1016/j.joi.2024.101530","DOIUrl":"https://doi.org/10.1016/j.joi.2024.101530","url":null,"abstract":"<div><p>This study investigates how scientific research influences policymaking by analyzing citations of research articles in policy documents (policy impact) for nearly 125,000 articles across 434 public policy journals. We reveal distinct citation patterns between policymakers and other stakeholders like researchers, journalists, and the public. News and blog mentions, social media engagement, and open access publications (excluding fully open access) significantly increase the likelihood of a research article being cited in policy documents. Conversely, articles locked behind paywalls and those published under the full open access model (based on Altmetric data) have a lower chance of being policy-cited. Publication year and policy type show no significant influence. Our findings emphasize the crucial role of science communication channels like news media and social media in bridging the gap between research and policy. Interestingly, academic citations hold a weaker influence on policy citations compared to news mentions, suggesting a potential disconnect between how researchers reference research and how policymakers utilize it. This highlights the need for improved communication strategies to ensure research informs policy decisions more effectively. This study provides valuable insights for researchers, policymakers, and science communicators. Researchers can tailor their dissemination efforts to reach policymakers through media channels. Policymakers can leverage these findings to identify research with higher policy relevance. Science communicators can play a critical role in translating research for policymakers and fostering dialogue between the scientific and policymaking communities.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 3","pages":"Article 101530"},"PeriodicalIF":3.7,"publicationDate":"2024-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1751157724000439/pdfft?md5=849582c88cb2a16f5ee8de12b745cd1e&pid=1-s2.0-S1751157724000439-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140551164","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":"Comparing semantic representation methods for keyword analysis in bibliometric research","authors":"Guo Chen ,&nbsp;Siqi Hong ,&nbsp;Chenxin Du ,&nbsp;Panting Wang ,&nbsp;Zeyu Yang ,&nbsp;Lu Xiao","doi":"10.1016/j.joi.2024.101529","DOIUrl":"https://doi.org/10.1016/j.joi.2024.101529","url":null,"abstract":"<div><p>Semantic representation methods play a crucial role in text mining tasks. Although numerous approaches have been proposed and compared in text mining research, the comparison of semantic representation methods specifically for publication keywords in bibliometric studies has received limited attention. This lack of practical evidence makes it challenging for researchers to select suitable methods to obtain keyword vectors for downstream bibliometric tasks, potentially hindering the achievement of optimal results. To address this gap, this study conducts an experimental comparison of various typical semantic representation methods for keywords, aiming to provide quantitative evidence for bibliometric studies. The experiment focuses on keyword clustering as the fundamental task and evaluates 22 variations of five typical methods across four scientific domains. The methods compared are co-word matrix, co-word network, word embedding, network embedding, and “semantic + structure” integration. The comparison is based on fitting the clustering results of these methods with the “evaluation standard” specific to each domain. The empirical findings demonstrate that the co-word matrix exhibits subpar performance, whereas the co-word network and word embedding techniques display satisfactory performance. Among the five network embedding algorithms, LINE and Node2Vec outperform DeepWalk, Struc2Vec, and SDNE. Remarkably, both the “pre-training and fine-tuning” model and the “semantic + structure” model yield unsatisfactory results in terms of performance. Nevertheless, even with variations in the performance of these methods, no singular approach stands out as universally superior. When selecting methods in practical applications, comprehensive consideration of factors such as corpus size and semantic cohesion of domain keywords is crucial. This study advances our understanding of semantic representation methods for keyword analysis and contributes to the advancement of bibliometric analysis by providing valuable recommendations for researchers in selecting appropriate methods.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 3","pages":"Article 101529"},"PeriodicalIF":3.7,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140348105","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}