Pub Date : 2025-02-12DOI: 10.1016/j.joi.2025.101644
Nils M. Denter, Joe Waterstraat, Martin G. Moehrle
Scientific knowledge plays a major role in the generation of new technological knowledge. We present a new novelty-driven approach to measure the influence of science on patents. We overcome the weaknesses of previous methods based on either citations or semantic similarities, both representing direct linkages between documents. We combine patent novelty measurement with technology-specific, scientific dictionaries, which allow us to measure a patent's nearness to science by stable indirect linkages. We apply our indicator “science-driven novelty” to the testbed of RFID technology and confirm its validity by conducting an expert survey. Subsequently, we test how science impacts patent value, finding that scientific influence increases the average value of a patent. Our results suggest several implications. For academics, we recommend not relying solely on analyzing direct links between papers and patents to determine the influence of science on technology. For management, we provide a new tool to assess scientific influences in patents and thus the value of their company's own patent portfolio as well as the portfolios of third parties. Using text as data, the tool is viable at a very early stage and can be helpful in go/no-go decisions for technology management.
{"title":"Avoiding the pitfalls of direct linkage: A novelty-driven approach to measuring scientific impact on patents","authors":"Nils M. Denter, Joe Waterstraat, Martin G. Moehrle","doi":"10.1016/j.joi.2025.101644","DOIUrl":"10.1016/j.joi.2025.101644","url":null,"abstract":"<div><div>Scientific knowledge plays a major role in the generation of new technological knowledge. We present a new novelty-driven approach to measure the influence of science on patents. We overcome the weaknesses of previous methods based on either citations or semantic similarities, both representing direct linkages between documents. We combine patent novelty measurement with technology-specific, scientific dictionaries, which allow us to measure a patent's nearness to science by stable indirect linkages. We apply our indicator “science-driven novelty” to the testbed of RFID technology and confirm its validity by conducting an expert survey. Subsequently, we test how science impacts patent value, finding that scientific influence increases the average value of a patent. Our results suggest several implications. For academics, we recommend not relying solely on analyzing direct links between papers and patents to determine the influence of science on technology. For management, we provide a new tool to assess scientific influences in patents and thus the value of their company's own patent portfolio as well as the portfolios of third parties. Using text as data, the tool is viable at a very early stage and can be helpful in go/no-go decisions for technology management.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101644"},"PeriodicalIF":3.4,"publicationDate":"2025-02-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143386456","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}
Pub Date : 2025-02-03DOI: 10.1016/j.joi.2025.101642
Juan Gorraiz
This study investigates the evolving role of AI tools, such as ChatGPT, in academic research, with a focus on whether these tools are recognized as authors or co-authors, and how their contributions are cited or acknowledged across various fields. Using data from two major bibliometric sources, Web of Science Core Collection and Scopus, the analysis reveals patterns of AI citation, co-authorship, and acknowledgments. While some attempts have been made to credit AI as a co-author, ethical guidelines—such as those from COPE—prevent this due to AI's inability to fulfill the intellectual requirements for authorship. Instead, AI is increasingly cited as a source or mentioned in acknowledgments to ensure transparency in its use. The study further addresses the ethical implications of AI's role in disrupting traditional notions of intellectual reciprocity and bibliometric analysis. The future role of AI in research will depend on how challenges related to access, equity, and intellectual contribution are managed, determining whether AI will democratize research or exacerbate existing inequalities.
本研究调查了人工智能工具（如ChatGPT）在学术研究中的演变作用，重点关注这些工具是否被认可为作者或共同作者，以及它们的贡献如何在各个领域被引用或认可。使用来自两个主要文献计量来源的数据，Web of Science Core Collection和Scopus，分析揭示了人工智能引文、共同作者和致谢的模式。虽然有些人试图将人工智能作为合著者，但伦理准则——比如来自cope的准则——阻止了这一点，因为人工智能无法满足作者的智力要求。相反，人工智能越来越多地被引用为来源或在致谢中提及，以确保其使用的透明度。该研究进一步探讨了人工智能在颠覆智力互惠和文献计量分析的传统观念方面所起的伦理影响。人工智能在研究中的未来作用将取决于如何管理与获取、公平和智力贡献相关的挑战，这决定了人工智能是会使研究民主化，还是会加剧现有的不平等。
{"title":"Acknowledging the new invisible colleague: Addressing the recognition of Open AI contributions in in scientific publishing","authors":"Juan Gorraiz","doi":"10.1016/j.joi.2025.101642","DOIUrl":"10.1016/j.joi.2025.101642","url":null,"abstract":"<div><div>This study investigates the evolving role of AI tools, such as ChatGPT, in academic research, with a focus on whether these tools are recognized as authors or co-authors, and how their contributions are cited or acknowledged across various fields. Using data from two major bibliometric sources, Web of Science Core Collection and Scopus, the analysis reveals patterns of AI citation, co-authorship, and acknowledgments. While some attempts have been made to credit AI as a co-author, ethical guidelines—such as those from COPE—prevent this due to AI's inability to fulfill the intellectual requirements for authorship. Instead, AI is increasingly cited as a source or mentioned in acknowledgments to ensure transparency in its use. The study further addresses the ethical implications of AI's role in disrupting traditional notions of intellectual reciprocity and bibliometric analysis. The future role of AI in research will depend on how challenges related to access, equity, and intellectual contribution are managed, determining whether AI will democratize research or exacerbate existing inequalities.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 2","pages":"Article 101642"},"PeriodicalIF":3.4,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143161105","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}
Pub Date : 2025-02-01DOI: 10.1016/j.joi.2025.101637
Zhixiang Wu , Hucheng Jiang , Lianjie Xiao , Hao Wang , Jin Mao
Scholars continuously explore new research topics to drive personal academic achievements. While factors influencing topic selection exist, the predictability of scholars’ choices regarding new topics is not yet fully understood. To bridge the gap, this study investigates the predictability of new topics of scholars (NTS). The research task is transformed into a binary classification, predicting whether NTS that appear in the disciplinary knowledge network will be adopted by a scholar in the future. Using PubMed Knowledge Graph (PKG) as the data source, over 17,000 local knowledge networks (LKNs) of individual scholars are constructed, along with a global knowledge network (GKN) of all the scholars in the database. Sixteen features of knowledge network topology and candidate topics are extracted, and seven machine learning algorithms are applied. Our large-scale experiments show that the best prediction model achieves an F1 score of 86.49%. Shapley values provide more interpretable results. A 1-year observation window appears to be sufficient for making predictions. Novel topics and young scholars exhibit good predictability. Our findings provide profound insights into the predictability of scholars' topic selection and offer practical implications for future in-depth studies.
{"title":"Study on the predictability of new topics of scholars: A machine learning-based approach using knowledge networks","authors":"Zhixiang Wu , Hucheng Jiang , Lianjie Xiao , Hao Wang , Jin Mao","doi":"10.1016/j.joi.2025.101637","DOIUrl":"10.1016/j.joi.2025.101637","url":null,"abstract":"<div><div>Scholars continuously explore new research topics to drive personal academic achievements. While factors influencing topic selection exist, the predictability of scholars’ choices regarding new topics is not yet fully understood. To bridge the gap, this study investigates the predictability of <em>new topics of scholars (NTS)</em>. The research task is transformed into a binary classification, predicting whether <em>NTS</em> that appear in the disciplinary knowledge network will be adopted by a scholar in the future. Using PubMed Knowledge Graph (PKG) as the data source, over 17,000 local knowledge networks (LKNs) of individual scholars are constructed, along with a global knowledge network (GKN) of all the scholars in the database. Sixteen features of knowledge network topology and candidate topics are extracted, and seven machine learning algorithms are applied. Our large-scale experiments show that the best prediction model achieves an F1 score of 86.49%. Shapley values provide more interpretable results. A 1-year observation window appears to be sufficient for making predictions. Novel topics and young scholars exhibit good predictability. Our findings provide profound insights into the predictability of scholars' topic selection and offer practical implications for future in-depth studies.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101637"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165448","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}
Pub Date : 2025-02-01DOI: 10.1016/j.joi.2024.101636
Jia Zhang , Jian Wang , Jos Winnink , Simcha Jong
This paper studies how tie strength and structural holes collectively affect innovation radicalness at a location within an innovating firm. We identified 16,011 inventors’ locations of the 93 most innovative U.S. pharmaceuticals and biotechnology companies on the EU Industrial R&D Investment Scoreboard. We tracked their patents from 2001 to 2013 and constructed a panel dataset for analysis. Using firm-location fixed effect models, we found that the average tie strength of a location's egocentric network has a negative effect on innovation radicalness, and this negative effect is stronger when the location's egocentric network is cohesive. This suggests that weak ties have informational advantages for radical innovation, which are more pronounced when there is network cohesion to mitigate the relational disadvantages of weak ties. We also found a negative effect of structural holes on innovation radicalness when tie strength is weak but a positive effect when tie strength is strong. This indicates that strong ties are needed for mobilizing the informational advantages associated with structural holes.
{"title":"Collaboration networks and radical innovation: Two faces of tie strength and structural holes","authors":"Jia Zhang , Jian Wang , Jos Winnink , Simcha Jong","doi":"10.1016/j.joi.2024.101636","DOIUrl":"10.1016/j.joi.2024.101636","url":null,"abstract":"<div><div>This paper studies how tie strength and structural holes collectively affect innovation radicalness at a location within an innovating firm. We identified 16,011 inventors’ locations of the 93 most innovative U.S. pharmaceuticals and biotechnology companies on the EU Industrial R&D Investment Scoreboard. We tracked their patents from 2001 to 2013 and constructed a panel dataset for analysis. Using firm-location fixed effect models, we found that the average tie strength of a location's egocentric network has a negative effect on innovation radicalness, and this negative effect is stronger when the location's egocentric network is cohesive. This suggests that weak ties have informational advantages for radical innovation, which are more pronounced when there is network cohesion to mitigate the relational disadvantages of weak ties. We also found a negative effect of structural holes on innovation radicalness when tie strength is weak but a positive effect when tie strength is strong. This indicates that strong ties are needed for mobilizing the informational advantages associated with structural holes.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101636"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165423","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}
Pub Date : 2025-02-01DOI: 10.1016/j.joi.2024.101635
Chi-shiou Lin , Mu-hsuan Huang , Dar-zen Chen
The phenomenon of multi-affiliation in scientific authorship has increasingly garnered attention in scholarly communication. This study examines the extent and implications of multi-affiliation, distinguishing between intra-institutional and inter-institutional multi-affiliations, including their subtypes based on national and international affiliations. Utilizing data from the Web of Science, the study analyzes scientific papers from well-ranked global universities over a decade (2013–2022). The results indicate a significant prevalence of multi-affiliation, with 22.54 % of authorships and over half of the papers exhibiting at least one instance of multi-affiliation. The study finds notable variations in multi-affiliation trends across countries and subject fields. The findings raise critical questions about the impact of multi-affiliation on research evaluation and university rankings, suggesting a need for refined bibliometric measures and author guidance on affiliations to account for this growing trend.
{"title":"The inter-institutional and intra-institutional multi-affiliation authorships in the scientific papers produced by the well-ranked universities","authors":"Chi-shiou Lin , Mu-hsuan Huang , Dar-zen Chen","doi":"10.1016/j.joi.2024.101635","DOIUrl":"10.1016/j.joi.2024.101635","url":null,"abstract":"<div><div>The phenomenon of multi-affiliation in scientific authorship has increasingly garnered attention in scholarly communication. This study examines the extent and implications of multi-affiliation, distinguishing between intra-institutional and inter-institutional multi-affiliations, including their subtypes based on national and international affiliations. Utilizing data from the Web of Science, the study analyzes scientific papers from well-ranked global universities over a decade (2013–2022). The results indicate a significant prevalence of multi-affiliation, with 22.54 % of authorships and over half of the papers exhibiting at least one instance of multi-affiliation. The study finds notable variations in multi-affiliation trends across countries and subject fields. The findings raise critical questions about the impact of multi-affiliation on research evaluation and university rankings, suggesting a need for refined bibliometric measures and author guidance on affiliations to account for this growing trend.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101635"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165447","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}
Pub Date : 2025-02-01DOI: 10.1016/j.joi.2024.101634
Anbang Du , Michael Head , Markus Brede
Interdisciplinary research fuels innovation. In this paper, we examine the interdisciplinarity of research output driven by funding. Considering 36 major infectious diseases, we model interdisciplinarity through temporal correlation networks based on funded and unfunded research from 1995-2022. Using hierarchical clustering, we identify coherent periods of time or regimes characterised by important research topics like vaccinations or the Zika outbreak. We establish that funded research is less interdisciplinary than unfunded research, but the effect has decreased markedly over time. In terms of network growth, we find a tendency of funded research to focus on readily established connections leading to compartmentalisation and conservatism. In contrast, unfunded research tends to be exploratory and bridge distant knowledge leading to knowledge integration. Our results show that interdisciplinary research on prominent infectious diseases like HIV and tuberculosis tends to have strong bridging effects facilitating global knowledge integration in the network. At the periphery of the network, we observe the emergence of vaccination-related and Zika-related knowledge clusters, both with limited systemic impact. We further show that despite the surge in publications related to COVID-19, its systematic impact on the disease network remains relatively low. Overall, this research provides a generalisable framework to examine the impact of funding in interdisciplinary knowledge creation. It can assist in priority setting, for example with horizon scanning for new and emerging threats to health, such as pandemic planning. Policymakers, funding agencies, and research institutions should consider revamping evaluation systems to reward interdisciplinary work and implement mechanisms that promote and support intelligent risk-taking.
{"title":"Integration vs segregation: Network analysis of interdisciplinarity in funded and unfunded research on infectious diseases","authors":"Anbang Du , Michael Head , Markus Brede","doi":"10.1016/j.joi.2024.101634","DOIUrl":"10.1016/j.joi.2024.101634","url":null,"abstract":"<div><div>Interdisciplinary research fuels innovation. In this paper, we examine the interdisciplinarity of research output driven by funding. Considering 36 major infectious diseases, we model interdisciplinarity through temporal correlation networks based on funded and unfunded research from 1995-2022. Using hierarchical clustering, we identify coherent periods of time or regimes characterised by important research topics like vaccinations or the Zika outbreak. We establish that funded research is less interdisciplinary than unfunded research, but the effect has decreased markedly over time. In terms of network growth, we find a tendency of funded research to focus on readily established connections leading to compartmentalisation and conservatism. In contrast, unfunded research tends to be exploratory and bridge distant knowledge leading to knowledge integration. Our results show that interdisciplinary research on prominent infectious diseases like HIV and tuberculosis tends to have strong bridging effects facilitating global knowledge integration in the network. At the periphery of the network, we observe the emergence of vaccination-related and Zika-related knowledge clusters, both with limited systemic impact. We further show that despite the surge in publications related to COVID-19, its systematic impact on the disease network remains relatively low. Overall, this research provides a generalisable framework to examine the impact of funding in interdisciplinary knowledge creation. It can assist in priority setting, for example with horizon scanning for new and emerging threats to health, such as pandemic planning. Policymakers, funding agencies, and research institutions should consider revamping evaluation systems to reward interdisciplinary work and implement mechanisms that promote and support intelligent risk-taking.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101634"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165372","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}
Pub Date : 2025-02-01DOI: 10.1016/j.joi.2024.101630
Kai Meng , Zhichao Ba , Chunying Wang , Gang Li
Artificial Intelligence (AI) is experiencing unprecedented innovation and transformation, potentially attributed to intimate interactions between science and technology (S&T) within the field. To identify S&T linkages and detect intrinsic interactions within AI, this paper introduces a network portrait divergence approach, where S&T knowledge networks are prototyped as two-dimensional network portraits based on graph-invariant probability distributions, and comparing them by coupling network portrait divergence with knowledge content. Specifically, S&T knowledge of AI is first extracted and unified through KeyBERT and word-alignment algorithms. Subsequently, temporal S&T knowledge networks are constructed and visualized as two network portraits: node portraits and edge-weight portraits. Network portrait divergence, an information-theoretic, graph-like measure for comparing networks, is applied to calculate varying S&T portrait divergences. Finally, internal knowledge flows within S&T and dynamic interactions between them are unearthed based on multiscale backbone analysis. Empirical experiments on both synthetic networks (random graph ensembles) and real-world AI datasets underscore the feasibility and reliability of the network portrait divergence approach.
{"title":"Unveiling intrinsic interactions of science and technology in artificial intelligence using a network portrait divergence approach","authors":"Kai Meng , Zhichao Ba , Chunying Wang , Gang Li","doi":"10.1016/j.joi.2024.101630","DOIUrl":"10.1016/j.joi.2024.101630","url":null,"abstract":"<div><div>Artificial Intelligence (AI) is experiencing unprecedented innovation and transformation, potentially attributed to intimate interactions between science and technology (S&T) within the field. To identify S&T linkages and detect intrinsic interactions within AI, this paper introduces a network portrait divergence approach, where S&T knowledge networks are prototyped as two-dimensional network portraits based on graph-invariant probability distributions, and comparing them by coupling network portrait divergence with knowledge content. Specifically, S&T knowledge of AI is first extracted and unified through KeyBERT and word-alignment algorithms. Subsequently, temporal S&T knowledge networks are constructed and visualized as two network portraits: node portraits and edge-weight portraits. Network portrait divergence, an information-theoretic, graph-like measure for comparing networks, is applied to calculate varying S&T portrait divergences. Finally, internal knowledge flows within S&T and dynamic interactions between them are unearthed based on multiscale backbone analysis. Empirical experiments on both synthetic networks (random graph ensembles) and real-world AI datasets underscore the feasibility and reliability of the network portrait divergence approach.</div></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"19 1","pages":"Article 101630"},"PeriodicalIF":3.4,"publicationDate":"2025-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143165370","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}
Pub Date : 2025-02-01DOI: 10.1016/j.joi.2024.101632
Guancan Yang , Di Liu , Ling Chen , Kun Lu
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 Separate Temporal Exponential Random Graph Model (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.
{"title":"Integrating persistence process into the analysis of technology convergence using STERGM","authors":"Guancan Yang , Di Liu , Ling Chen , 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}
Pub Date : 2025-02-01DOI: 10.1016/j.joi.2024.101631
David Melero-Fuentes , Remedios Aguilar-Moya , Juan-Carlos Valderrama-Zurián , Juan Gorraiz
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.
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.
The results obtained show that in disciplines such as Clinical Medicine, Neuroscience & Behavior. and Biology & Biochemistry, 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 Oncology and Hematology.
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.
本研究的目的是分析“会议摘要”这一文件类型在数据库（即Web of Science核心馆藏）中最具代表性的存在和演变。我们还研究了他们在哪些类别和哪种类型的期刊上有重要的存在。对会议摘要的年份、指标、类别和主题变量进行频率分析（绝对值和比率），计算影响因子（Impact Factor），不考虑会议摘要获得的引用。结果表明，在临床医学、神经科学等学科中；的行为。和生物&；它们在生物化学中扮演着非常重要的角色，因为它们的数量和吸引引用的数量，而且它们经常发表在顶级期刊上，根据《期刊引用报告》（Journal of Citation Reports），包括Q1。我们的研究结果也证实了一个假设，即它们夸大了影响因子，因此是该指标在肿瘤学和血液学等类别中绝对值高的原因之一。本研究对研究人员和政策制定者具有重要意义，因为它有助于确定哪些学科的会议摘要具有相关性，并且在文献计量实践中应该考虑这些指标的计算，并为研究不同科学科学传播社会过程中会议摘要与其他文献类型学的关系打开了大门。
{"title":"Evolution and effect of meeting abstracts in JCR journals","authors":"David Melero-Fuentes , Remedios Aguilar-Moya , Juan-Carlos Valderrama-Zurián , 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 & Behavior.</em> and <em>Biology & 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 , Jiaxin Xing , Shuo Xu , 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}
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