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关键词： 应用语言学   研究生   实验研究   方法论   学术能力  

摘要： 通过对应用语言学研究生学位论文质量研究的回顾,探讨了提高应用语言学实验研究质量的关键问题。以实验研究代表性专著《理解与评价应用语言学实验研究》为主要依据,对研究生方法论课程教学提出了一些建议。具体而言,在教学中应重视案例教学,突出实验设计的重要性,把握构念的定义与操作之间的逻辑联系。建议开设统计学课程,建立较为系统的论文写作指导体系,以期在实验研究方面进一步提高研究生的学术能力。

关键词： 命令形  

关键词： 生态语言学连续统   多维协同连续统模型   互文语境   生态语境化  

摘要： 鉴于现有生态语言学理论范式的局限性,本文提出生态语言学连续统构念,阐述其构成要素及其整体性与多样性、动态性与平衡性、关联性与持续性、协调性与进化性等本体论特征,并据此建构词汇语法-语篇-语境的多维协同连续统模型,以话语标记的显性-隐性标记连续统为例加以验证。结果表明:1)多维协同实验组的语篇识解优于无多维协同对照组,组间差异显著;2)多维连续统协同的隐性标记产出效果组间差异显著,实验组优于对照组,且更接近本族语者使用规范,但两组都表现出显性-隐性标记连续统发展特征。上述发现验证了该模型的合理性和有效性,不仅为生态语言学连续统理念提供了有力证据,也为二语发展提供了生态语境化教学启示。

关键词： 突发公共事件   网络舆情   应急语言   社会语言学  

摘要： 应急语言能力作为一种特殊的语言能力,在突发公共事件发生时可以借助言语沟通解决各种危机。2020年1月以来,由于新冠肺炎疫情暴发,引发了网络空间内的一系列语言接触和语言冲突,造成网络舆情的裂变。文章全面梳理网络舆情的研究视角,在此基础上,从社会语言学言语行为理论出发,对社会化媒体微博进行语料收集和分析,总结社会化媒体的应急语言策略,以期为突发公共事件的网络舆情管理提供语言援助。

关键词： 认知语言学   大学英语   口语   教学模式  

摘要： 该文根据认知语言学理论基础,分析了在大学口语教学模式中相关理论与方法的实践价值。认知语言学理论包括涉身观、构式观、概念馆以及使用观,教学原则包括依托体验原则、词汇——语法连续体原则、概念化原则以及使用原则。

关键词： 社会语言学   语言与社会   《窈窕淑女》   语言的使用   语体差异   词汇和句法   语言本身   交际手段  

摘要： 社会语言学是研究语言与社会关系的学科,特别关注语言在社会中的使用方式,以及人们在使用语言时的选择。作为社会语言学的一个重要课题,本文将讨论语言与社会地位之间的关系。作为最常见的交际手段,语言本身是客观存在的,而语言的使用在发音、词汇、语法等方面却往往呈现出模式化的变化,在不同的社会环境中会有不同的表现。语言随着使用者的不同而变化,也随着使用环境的不同而变化。本文将主要从语体差异的角度,从语音、词汇和句法三个层面对音乐剧《窈窕淑女》的语言与社会地位的相关性进行分析。

摘要： 第十二届中国认知语言学研讨会将于2021年10月16至17日在泉城济南召开。本次会议由山东财经大学外国语学院承办,主题为“认知语言学:前沿问题与领域拓展”,分议题为认知语言学的传统问题、前沿问题以及研究领域的拓展,包括(但不限于): 隐喻与转喻、心理空间与概念整合、认知语法与构式语法、认知范畴化、体验认知、认知识解、认知语义学、认知语言学与系统功能语言学、语料库、语用学、心理语言学、神经语言学、第一/第二语言习得、汉语研究、话语研究、社会语言学、语言类型学、历史语言学、文学、词汇学、音位学、句法学、手势研究、人工智等能学科的交叉研究。本次研讨会设主旨发言、专题研讨、小组发言等形式。专题研讨每场2小时,小组发言每人20分钟,10分钟问答环节。请将论文摘要WORD版(300字以内)发送到会议专用邮箱。

关键词： Gender   Pediatric Surgery   Fellowship Match   fellowship training   Linguistic Inquiry and Word Count Software  

摘要： Background: : Gender bias in letters of recommendation (LOR) has been reported for candidate selection in academic medicine, typically with females frequently described with communal terms (e.g. helpful, kind, interpersonal) and males described more with agentic terms (e.g. assertive, intellectual, ambitious). This study examined the presence of linguistic gender differences in LOR for Pediatric Surgery Fellowship. Methods: : LOR submitted to a single pediatric surgery fellowship program between 2014 and 2018 were retrospectively reviewed and analyzed using a previously validated Linguistic Inquiry and Word Count Software (LIWC) program. Descriptive statistics and bivariate analysis were employed in our analysis. Multivariable logistic regression models were built to assess independent association of LIWC variables with applicant gender, LOR writer gender, and applicant-writer gender concordance. Results: : 1264 LOR from 325 applicants (51% female) were analyzed. Of the letter writers, 83% were male, 57% were professors, and 7.6% were Pediatric Surgery Fellowship Program Directors. The overall median average word count was 518 words, with no significant difference in LOR word count between ap-plicant genders. Compared to male applicants, female applicants were described significantly more with work words (e.g. excellent, work;p = 0.04). Male LOR writers used authentic words (e.g. honest, humble;p = 0.006) and home words (e.g. family, house;p = 0.04) significantly more than female LOR writers. There were no significant differences in the use of agentic and communal words between genders. Conclusion: : While there are linguistic differences in LOR for candidates in the Pediatric Surgery match based on gender, previously described overt gender bias was not seen in this study. Interestingly, fe-male candidates were described more with work words, like excellent;a reversal of previously described gender bias in academic medicine. These findings may be due to the unique i

摘要： This special issue aims to explore the advances in historical linguistics that are made possible by the ongoing documentation of underdescribed and endangered languages. About this special issue The present special issue evolved out of the two-day workshop I From fieldwork to reconstruction: Language documentation and historical linguistics i , convened by the editor on 7-8 November 2018 at the Language and Culture Research Centre (James Cook University), Cairns, Australia. Although communication during the pandemic has highlighted many linguistic barriers and a huge divide between hegemonic and minority languages (see López 2020 for Latin America), there has been an unprecedented worldwide production of health information materials in indigenous languages (see CEJIS 2020, among others, for some examples in Bolivian languages), which was also made possible by linguistic research. Meanwhile, the COVID-19 pandemic has made the description of minority languages even more urgent, as coronavirus has often hit the hardest those vulnerable communities in which endangered languages are spoken, causing the death of many language keepers. [Extracted from the article]

关键词： Bayesian approximation   cloud vendors   comprehensive method   Maclaurin symmetric mean   multicriteria decision‐   making  

摘要： Cloud vendors (CVs) play an indispensable role in the development of IT sectors and industry 4.0. Many CVs evolve every day, and a systematic selection of these is becoming substantial for organizations. Literature studies have shown that multicriteria decision-making (MCDM) is a powerful tool for systematic selection. However, the major issue with the state-of-the-art models is that they do not effectively represent uncertainty. Moreover, the personalized selection of CVs based on user queries is not prominent in an MCDM context. In this paper, to circumvent these issues, a new decision framework is proposed that utilizes a generalized preference style called interval-valued probabilistic linguistic term set (IVPLTS). This preference style considers occurring probability values as interval numbers instead of a single precise value, which provides flexibility during preference elicitation. Initially, missing values are imputed systematically by using a case-based method. Then, the consistency of these preferences is checked using Cronbach's alpha coefficient, and the inconsistent preferences are repaired rationally by using an iterative method. A programming model is proposed for determining the weights of the evaluation criteria. Furthermore, Maclaurin symmetric mean (MSM) is extended to IVPLTS for aggregating preferences from each expert. The interval-valued probabilistic linguistic comprehensive (IVPLC) method is proposed for prioritizing CVs in a personalized manner. Finally, the framework's practicality is validated by using a case study of CV selection for an academic institution;strengths and weaknesses of the framework are conferred by comparison with extant CV selection models.