{"title":"应用语言学研究的科学计量分析(1970-2022):方法与未来方向","authors":"Azrifah Zakaria, Vahid Aryadoust","doi":"10.1515/applirev-2022-0210","DOIUrl":null,"url":null,"abstract":"Abstract In this study, we provide a scientometric analysis of 43,685 studies published in 51 quartile-1 journals in the field of applied linguistics (1970–2022). Scientometric analysis uses citation records to quantitatively compute networks of cited works and map out how published works have been cited. We adapted a multi-stage scientometric method consisting of database identification, dataset generation, document co-citation analysis, research cluster identification, and cluster characterization. A number of major research clusters were identified and a high degree of interconnectedness in terms of theoretical base was observed between the clusters. The pre-2000 publications had a conspicuous focus on theories derived from language use, which might be said had set the tone for the maturation of the field. By contrast, the clusters that emerged from the 2000s showed more specificity and granularity in focus and scope, suggesting the beginning of a research era with more specialized directions. Despite this trend, we identified influential publications which received several spikes in citations in different eras, indicating their continued temporal and thematic relevance in different clusters. In addition, we found evidence of inter-cluster cross-pollinations. We discuss how each cluster should be characterized in terms of its knowledge base and knowledge front. Highly cited works form the knowledge base of a cluster while novel works form the knowledge fronts of a cluster. Future directions are mentioned and highlighted.","PeriodicalId":46472,"journal":{"name":"Applied Linguistics Review","volume":" ","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A scientometric analysis of applied linguistics research (1970–2022): methodology and future directions\",\"authors\":\"Azrifah Zakaria, Vahid Aryadoust\",\"doi\":\"10.1515/applirev-2022-0210\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract In this study, we provide a scientometric analysis of 43,685 studies published in 51 quartile-1 journals in the field of applied linguistics (1970–2022). Scientometric analysis uses citation records to quantitatively compute networks of cited works and map out how published works have been cited. We adapted a multi-stage scientometric method consisting of database identification, dataset generation, document co-citation analysis, research cluster identification, and cluster characterization. A number of major research clusters were identified and a high degree of interconnectedness in terms of theoretical base was observed between the clusters. The pre-2000 publications had a conspicuous focus on theories derived from language use, which might be said had set the tone for the maturation of the field. By contrast, the clusters that emerged from the 2000s showed more specificity and granularity in focus and scope, suggesting the beginning of a research era with more specialized directions. Despite this trend, we identified influential publications which received several spikes in citations in different eras, indicating their continued temporal and thematic relevance in different clusters. In addition, we found evidence of inter-cluster cross-pollinations. We discuss how each cluster should be characterized in terms of its knowledge base and knowledge front. Highly cited works form the knowledge base of a cluster while novel works form the knowledge fronts of a cluster. Future directions are mentioned and highlighted.\",\"PeriodicalId\":46472,\"journal\":{\"name\":\"Applied Linguistics Review\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2023-01-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Linguistics Review\",\"FirstCategoryId\":\"98\",\"ListUrlMain\":\"https://doi.org/10.1515/applirev-2022-0210\",\"RegionNum\":2,\"RegionCategory\":\"文学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"0\",\"JCRName\":\"LANGUAGE & LINGUISTICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Linguistics Review","FirstCategoryId":"98","ListUrlMain":"https://doi.org/10.1515/applirev-2022-0210","RegionNum":2,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"LANGUAGE & LINGUISTICS","Score":null,"Total":0}
A scientometric analysis of applied linguistics research (1970–2022): methodology and future directions
Abstract In this study, we provide a scientometric analysis of 43,685 studies published in 51 quartile-1 journals in the field of applied linguistics (1970–2022). Scientometric analysis uses citation records to quantitatively compute networks of cited works and map out how published works have been cited. We adapted a multi-stage scientometric method consisting of database identification, dataset generation, document co-citation analysis, research cluster identification, and cluster characterization. A number of major research clusters were identified and a high degree of interconnectedness in terms of theoretical base was observed between the clusters. The pre-2000 publications had a conspicuous focus on theories derived from language use, which might be said had set the tone for the maturation of the field. By contrast, the clusters that emerged from the 2000s showed more specificity and granularity in focus and scope, suggesting the beginning of a research era with more specialized directions. Despite this trend, we identified influential publications which received several spikes in citations in different eras, indicating their continued temporal and thematic relevance in different clusters. In addition, we found evidence of inter-cluster cross-pollinations. We discuss how each cluster should be characterized in terms of its knowledge base and knowledge front. Highly cited works form the knowledge base of a cluster while novel works form the knowledge fronts of a cluster. Future directions are mentioned and highlighted.