{"title":"以共同引用的方式分析科学知识与技术知识之间的相互作用","authors":"Xi Chen , Jin Mao , Gang Li","doi":"10.1016/j.joi.2024.101548","DOIUrl":null,"url":null,"abstract":"<div><p>A systematic understanding of the interaction between science and technology is beneficial for innovation policies aimed at improving the utilization of science to advance technological development. Traditional approaches primarily focus on direct citation-based linkages, often overlooking the complex, evolving nature of the interaction between scientific and technological knowledge (S&T knowledge interaction). To address this issue, we proposed a novel methodological framework utilizing co-citations between patents and papers, offering a more comprehensive insight into the S&T knowledge interaction. First, we measured the linkage between scientific and technological knowledge based on co-citations between patents and papers. Then, we identified interaction communities and analyzed their evolution. This method not only captures the potential linkages between patents and papers, but also reveals consolidated interactions and rapid changes in S&T knowledge interaction. The results highlight distinct phases in the evolution of S&T knowledge interaction, which are instrumental for understanding how S&T knowledge interaction evolve, especially in rapidly advancing fields like genetic engineering. The insights gained are crucial for academics and practitioners in anticipating future trends and navigating the evolving landscape of science and technology.</p></div>","PeriodicalId":48662,"journal":{"name":"Journal of Informetrics","volume":"18 3","pages":"Article 101548"},"PeriodicalIF":3.4000,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A co-citation approach to the analysis on the interaction between scientific and technological knowledge\",\"authors\":\"Xi Chen , Jin Mao , Gang Li\",\"doi\":\"10.1016/j.joi.2024.101548\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>A systematic understanding of the interaction between science and technology is beneficial for innovation policies aimed at improving the utilization of science to advance technological development. Traditional approaches primarily focus on direct citation-based linkages, often overlooking the complex, evolving nature of the interaction between scientific and technological knowledge (S&T knowledge interaction). To address this issue, we proposed a novel methodological framework utilizing co-citations between patents and papers, offering a more comprehensive insight into the S&T knowledge interaction. First, we measured the linkage between scientific and technological knowledge based on co-citations between patents and papers. Then, we identified interaction communities and analyzed their evolution. This method not only captures the potential linkages between patents and papers, but also reveals consolidated interactions and rapid changes in S&T knowledge interaction. The results highlight distinct phases in the evolution of S&T knowledge interaction, which are instrumental for understanding how S&T knowledge interaction evolve, especially in rapidly advancing fields like genetic engineering. The insights gained are crucial for academics and practitioners in anticipating future trends and navigating the evolving landscape of science and technology.</p></div>\",\"PeriodicalId\":48662,\"journal\":{\"name\":\"Journal of Informetrics\",\"volume\":\"18 3\",\"pages\":\"Article 101548\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2024-06-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Informetrics\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S1751157724000610\",\"RegionNum\":2,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Informetrics","FirstCategoryId":"91","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1751157724000610","RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
A co-citation approach to the analysis on the interaction between scientific and technological knowledge
A systematic understanding of the interaction between science and technology is beneficial for innovation policies aimed at improving the utilization of science to advance technological development. Traditional approaches primarily focus on direct citation-based linkages, often overlooking the complex, evolving nature of the interaction between scientific and technological knowledge (S&T knowledge interaction). To address this issue, we proposed a novel methodological framework utilizing co-citations between patents and papers, offering a more comprehensive insight into the S&T knowledge interaction. First, we measured the linkage between scientific and technological knowledge based on co-citations between patents and papers. Then, we identified interaction communities and analyzed their evolution. This method not only captures the potential linkages between patents and papers, but also reveals consolidated interactions and rapid changes in S&T knowledge interaction. The results highlight distinct phases in the evolution of S&T knowledge interaction, which are instrumental for understanding how S&T knowledge interaction evolve, especially in rapidly advancing fields like genetic engineering. The insights gained are crucial for academics and practitioners in anticipating future trends and navigating the evolving landscape of science and technology.
期刊介绍:
Journal of Informetrics (JOI) publishes rigorous high-quality research on quantitative aspects of information science. The main focus of the journal is on topics in bibliometrics, scientometrics, webometrics, patentometrics, altmetrics and research evaluation. Contributions studying informetric problems using methods from other quantitative fields, such as mathematics, statistics, computer science, economics and econometrics, and network science, are especially encouraged. JOI publishes both theoretical and empirical work. In general, case studies, for instance a bibliometric analysis focusing on a specific research field or a specific country, are not considered suitable for publication in JOI, unless they contain innovative methodological elements.