{"title":"事件分割与因果关系:以普通话因果链运动为例*","authors":"Yu Deng, F. Li","doi":"10.1111/stul.12211","DOIUrl":null,"url":null,"abstract":"","PeriodicalId":46179,"journal":{"name":"STUDIA LINGUISTICA","volume":"19 1","pages":""},"PeriodicalIF":0.4000,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Event Segmentation and Causation: The Case of Mandarin\\n Causal‐Chain\\n Motion*\",\"authors\":\"Yu Deng, F. Li\",\"doi\":\"10.1111/stul.12211\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\",\"PeriodicalId\":46179,\"journal\":{\"name\":\"STUDIA LINGUISTICA\",\"volume\":\"19 1\",\"pages\":\"\"},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2023-03-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"STUDIA LINGUISTICA\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1111/stul.12211\",\"RegionNum\":3,\"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":"STUDIA LINGUISTICA","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1111/stul.12211","RegionNum":3,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"LANGUAGE & LINGUISTICS","Score":null,"Total":0}
期刊介绍:
Studia Linguistica is committed to the publication of high quality, original papers and provides an international forum for the discussion of theoretical linguistic research, primarily within the fields of grammar, cognitive semantics and language typology. The principal aim is to open a channel of communication between researchers operating in traditionally diverse fields while continuing to focus on natural language data.