<p>I am not a specialist in digital communication, and instead I do interactional sociolinguistics and linguistic ethnography (henceforth ‘IS’ and ‘LE’), a syncretic research programme that draws <i>inter alia</i> on linguistic anthropology, conversation analysis and Goffman, usually cross-referring to relevant work in other disciplines (Rampton, <span>2022</span>).<sup>1</sup> IS/LE centres on the careful ethnographic observation, recording and analysis of embodied communication, and it investigates communication's embedding in a layered and interweaving multiplicity of social, cultural and material systems and processes. Depending on the questions and arguments that it is addressing, IS/LE certainly varies both in the forms of semiosis and the systems that it attends to (and I myself have tended to focus on spoken interaction in recreational and educational locales affected by racism, social class and securitisation). But when Helen Kelly-Holmes asks whether artificial intelligence (AI) now departs from ‘the known world for sociolinguistics’, it is a timely opportunity to reflect on the possibilities for—or indeed possibility <i>of</i>—IS/LE.</p><p>Kelly-Holmes speaks of a ‘seismic shift’ in the scholarly universe, citing Jan Blommaert (<span>2017</span>, p. 7), who also advocated ‘an acute eye for change’, saying that ‘reality changes. The bastard changes all the time; society refuses to sit still’ (Blommaert & Van de Aa, <span>2020</span>, p. 6). But Blommaert could also be rather equivocal about the significance of these changes, and elsewhere recommends less spectacular adaptations—‘we have to adjust… [and] what you do needs to be relevant, so don't go for the big recipes’ (2020, p. 6). Not knowing more about AI, I cannot gauge authoritatively the magnitude of its implications for IS/LE, but pursuing Blommaert's more muted second option, I can see several ways in which AI can remain a researchable empirical topic (Rampton, <span>2016</span>, pp. 314–324).</p><p>A lot of this can be studied empirically (Georgakopoulou et al., <span>2020</span>). Yes, these technological developments are likely to stretch IS/LE's methodological repertoire, also warranting the formation of new interdisciplinary collaborations with, for example, different kinds of computer scientist. But digital processes like these are actually drawn into analytical salience by one of IS/LE's foundational preoccupations: ‘algorithmic knowledge and media ideologies are now – alongside other semiotic resources and language ideologies – central in <i>having a voice</i>’ (Maly, <span>2022</span>, p. 15; emphasis added).</p><p>At least two terms figure prominently in this shift, driven by reckonings not only with AI but also with coloniality and the climate emergency (Chakrabarty, <span>2012</span>; Kell & Budach, <span>2024</span>; Pennycook, <span>2018</span>). Post-humanism is one, inter alia proposing a ‘philosophical critique of the Western humanist ideal of the “man o
布raidotti本人也倾向于希望，她利用一种审慎的 "技术癖"，期待人工智能技术 "解放甚至超越的潜力"（Braidotti, 2016, pp.16-17）。但在吉尔罗伊看来，"当务之急是，与其关心我们想象中的自己是谁或自己是什么，不如关心在当今的紧急状况下以及在肯定会等待我们的更严峻的环境中，我们能为彼此做些什么"（2019）。这一观点与布罗莫特的观点不谋而合："每当我怀疑自己在学术上所做工作的重要性时（相信我，这种情况经常发生），回答我的是......活动家、工会人士......学校教师"（布罗莫特 & Van de Aa, 2020, p.6）。同样，自 IS/LE 诞生以来，它就一直以 "手工 "实践参与为导向（如 Gumperz 等人，1979 年）。因此，IS/LE 与其他几种方法一样，是一种综合的社会语言学方法，用于分析不同事物如何在不断发展的当下结合和发展，尽管它不断向新的方向延伸，但我看不到它的价值有任何消失的迹象。这一点很重要，因为除了研究之外，这种方法的持续可信性还体现在凯利-霍姆斯提出的紧迫问题的另一个重要场合--我们的教学中，在教学中，人工智能已经在推动一场意义深远的反思（鲁道夫等人，2023；温盖特等人，2024）、鉴于人工智能可以如此流畅地写出一篇文章，论文作为给学生评分的方式现在看来已经死了（除非回到纸笔考试的考场），而语音学、功能语法或会话分析等技术方面的培训现在可能不那么需要关注了，因为正如凯利-霍姆斯的论文中 "聊天 "淡淡地说的那样，人工智能可以做越来越多的分析工作。当然，让学生了解不同框架中的基本理论和方法仍然很重要，当不同的框架结合起来分析具体的交际实践条块时（如在 IS/LE 中），这一点可能最为明显（在课堂上，数据课程非常适合探索它们的不同承受能力（Rampton & Van de Putte：第 7 节）。但是，教学的重中之重是说服学生关心我们课程中涉及的问题空间，激励学生真正参与到学习中来，慢慢地、深思熟虑地、反思性地、经常协作性地沉浸到论点、经验、理论、方法和数据中5 。但是，人工智能的灵活性也要求关注学生所在的机构环境。许多学生通过兼职来维持生活，作业多而时间少，将任务交给人工智能的压力会越来越大。因此，我们需要为学生腾出空间，让他们自己去体验、讨论、阅读和思考，确保我们不会过度教学和评估，即使这需要与新自由主义学院的一些绩效和审计压力作斗争（佩里，2023 年）。即便如此，在实际研究中，IS/LE 也不是一个 "大配方"。但是，只要有体现人类交流的地方，它（以及类似的工作）仍然可以成为一种有用的 "成分"：揭示被忽视的交流资源和隐藏的浪费，以不可预知的方式与其他观点进行对话，与学生一起探索本地实践和手头事物对更广泛问题的启示。
{"title":"Existential challenges and interactional sociolinguistics/linguistic ethnography","authors":"Ben Rampton","doi":"10.1111/josl.12685","DOIUrl":"https://doi.org/10.1111/josl.12685","url":null,"abstract":"<p>I am not a specialist in digital communication, and instead I do interactional sociolinguistics and linguistic ethnography (henceforth ‘IS’ and ‘LE’), a syncretic research programme that draws <i>inter alia</i> on linguistic anthropology, conversation analysis and Goffman, usually cross-referring to relevant work in other disciplines (Rampton, <span>2022</span>).<sup>1</sup> IS/LE centres on the careful ethnographic observation, recording and analysis of embodied communication, and it investigates communication's embedding in a layered and interweaving multiplicity of social, cultural and material systems and processes. Depending on the questions and arguments that it is addressing, IS/LE certainly varies both in the forms of semiosis and the systems that it attends to (and I myself have tended to focus on spoken interaction in recreational and educational locales affected by racism, social class and securitisation). But when Helen Kelly-Holmes asks whether artificial intelligence (AI) now departs from ‘the known world for sociolinguistics’, it is a timely opportunity to reflect on the possibilities for—or indeed possibility <i>of</i>—IS/LE.</p><p>Kelly-Holmes speaks of a ‘seismic shift’ in the scholarly universe, citing Jan Blommaert (<span>2017</span>, p. 7), who also advocated ‘an acute eye for change’, saying that ‘reality changes. The bastard changes all the time; society refuses to sit still’ (Blommaert & Van de Aa, <span>2020</span>, p. 6). But Blommaert could also be rather equivocal about the significance of these changes, and elsewhere recommends less spectacular adaptations—‘we have to adjust… [and] what you do needs to be relevant, so don't go for the big recipes’ (2020, p. 6). Not knowing more about AI, I cannot gauge authoritatively the magnitude of its implications for IS/LE, but pursuing Blommaert's more muted second option, I can see several ways in which AI can remain a researchable empirical topic (Rampton, <span>2016</span>, pp. 314–324).</p><p>A lot of this can be studied empirically (Georgakopoulou et al., <span>2020</span>). Yes, these technological developments are likely to stretch IS/LE's methodological repertoire, also warranting the formation of new interdisciplinary collaborations with, for example, different kinds of computer scientist. But digital processes like these are actually drawn into analytical salience by one of IS/LE's foundational preoccupations: ‘algorithmic knowledge and media ideologies are now – alongside other semiotic resources and language ideologies – central in <i>having a voice</i>’ (Maly, <span>2022</span>, p. 15; emphasis added).</p><p>At least two terms figure prominently in this shift, driven by reckonings not only with AI but also with coloniality and the climate emergency (Chakrabarty, <span>2012</span>; Kell & Budach, <span>2024</span>; Pennycook, <span>2018</span>). Post-humanism is one, inter alia proposing a ‘philosophical critique of the Western humanist ideal of the “man o","PeriodicalId":51486,"journal":{"name":"Journal of Sociolinguistics","volume":"28 5","pages":"38-43"},"PeriodicalIF":1.5,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/josl.12685","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Artificial intelligence and the future of sociolinguistic research: An African contextual review","authors":"Patience Afrakoma hMensa","doi":"10.1111/josl.12679","DOIUrl":"https://doi.org/10.1111/josl.12679","url":null,"abstract":"","PeriodicalId":51486,"journal":{"name":"Journal of Sociolinguistics","volume":"28 5","pages":"26-30"},"PeriodicalIF":1.5,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691210","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>Helen Kelly-Holmes’ call to explore the implications for sociolinguistics arising from the increased commercially driven digitalization of society is very timely. Like Kelly-Holmes, we share the view that the growing prevalence of online and artificial intelligence (AI) technologies in all aspects of our lives requires a critical assessment of assumptions, approaches, and practices that have grounded sociolinguistic research since its inception. While our discussion confirms Helen's observations, we also urge the development of a general critical attitude toward understanding language as digital data. The starting point for our argument is Helen's claim that there is an erasure of “authentic” languages from public digital spaces, “making it more difficult to gather data on real usage because it would be necessary to rely on public areas and/or negotiate access to these private spaces” (p. 5). For us, her observation brings to the fore that treating language as data has always been problematic. We want to raise two issues: the general epistemological limitations of using digital user data as a representation of language and community, and the consequent need for methods that take seriously the study of language in its social, political, and technological context. We suggest ethnography as a method for understanding what speakers actually do, and an opening of language research to also consider the workings and socio-political embeddings of digital and generative AI language technologies. Our discussion is in the spirit of a joint fruitful and constructive debate.</p><p>Let us start with a general critique of approaching language as “data” that correlates with social groups, which is so far a neglected aspect in the debates surrounding language, sociolinguistics, and AI. Historically, this discussion links to the colonial backgrounds of Western science and linguistics specifically. Colonial or missionary linguistic research (e.g., Deumert & Storch, <span>2020</span>; Errington, <span>2008</span>) demonstrates that dominant Western epistemologies of language and research methods in linguistics were shaped during the period of European colonialism. An important legacy of European colonialism is that it “sought to fundamentally change and reorganize the social and economic order of the societies it colonized, as opposed to satisfy itself with extracting tribute” (Couldry & Mejias, <span>2019</span>, p. 70). Part of this endeavor involved language “development” activities aimed at the goal of Bible translation and turning the colonized into Christian disciples. This was based on constructions of language that are still dominant today. They developed on the grounds of “collecting data” (in colonial times, often from single speakers) and then transforming the human capacity of embodied, interactive and collaborative meaning-making into word lists, grammar books, or dictionaries (e.g., Deumert & Storch, <span>2020</span>; Gal & Irvine,
海伦-凯利-霍尔姆斯（Helen Kelly-Holmes）呼吁探讨商业驱动的社会数字化对社会语言学的影响，这一呼吁非常及时。与凯利-霍尔姆斯一样，我们也认为，随着网络和人工智能（AI）技术在我们生活的方方面面日益普及，我们需要对社会语言学研究自诞生以来的假设、方法和实践进行批判性评估。我们的讨论证实了海伦的观点，同时我们也敦促大家对理解作为数字数据的语言形成一种普遍的批判态度。我们论证的出发点是海伦的主张，即 "正宗 "语言在公共数字空间中被抹去，"这使得收集真实使用情况的数据变得更加困难，因为有必要依靠公共区域和/或通过协商进入这些私人空间"（第 5 页）。对我们来说，她的观点让我们意识到，将语言作为数据处理一直是个问题。我们想提出两个问题：将数字用户数据作为语言和社区代表的一般认识论局限性，以及因此需要认真研究社会、政治和技术背景下的语言的方法。我们建议将人种学作为了解说话者实际行为的一种方法，并将语言研究扩展到对数字和生成式人工智能语言技术的运作和社会政治嵌入的考虑。让我们先对将语言作为与社会群体相关联的 "数据 "的做法进行一般性批判，迄今为止，在围绕语言、社会语言学和人工智能的争论中，这是一个被忽视的方面。从历史上看，这种讨论与西方科学和语言学的殖民背景有关。殖民地或传教士的语言学研究（如 Deumert & Storch, 2020; Errington, 2008）表明，西方占主导地位的语言认识论和语言学研究方法是在欧洲殖民主义时期形成的。欧洲殖民主义的一个重要遗产是，它 "试图从根本上改变和重组其殖民地社会的社会和经济秩序，而不是满足于榨取贡品"（Couldry & Mejias, 2019, p.70）。这种努力的一部分涉及语言 "发展 "活动，目的是翻译《圣经》并将殖民者变成基督教门徒。这些活动的基础是至今仍占主导地位的语言结构。它们是在 "收集数据 "的基础上发展起来的（在殖民地时代，数据往往来自单个说话者），然后将人类体现性、互动性和协作性的意义生成能力转化为词表、语法书或词典（例如，Deumert & Storch, 2020; Gal & Irvine, 2019, Chap.9）。因此，语言学研究的基础是现在所谓的 "数据主义 "意识形态（Bode & Goodlad, 2023）。这就是相信数据代表人类行为。在数字技术时代，这与追踪人类行为以预测并最终塑造社会生活的目标相结合（Rushkoff，2019 年）。数据主义意味着对量化客观性的信念和对数据处理代理的信任等假设。由此产生的日常生活数据化包括从社会生活流中提取信息，将其与想象的社会现实和类别相匹配，并固定这种关系。在语言学方面，将语言理解为从口头实践中收集并转化为文字的数据，导致将语言概念化为指代代码，将语言概念化为 "自然的"、有系统的、结构整齐的给定对象（例如，Pennycook，2004 年）。这些活动的成果归纳为类型学、发展等级以及优化数据提取和分析的方法（批判性讨论见 Deumert & Storch, 2020）。社会语言学是这一传统的一部分。但与旨在通过语言活动将自己的社会想象强加于人的传教士不同，社会语言学家的目标是发现和解释人们如何使用语言，特别是变体，以加深我们对语言及其社会文化嵌入性的理解，并提高人们的意识和反对歧视。早期的研究工作将人们的语言实践归纳为外部定义的、同质化的宏观社会（如年龄、阶级）和结构性语言类别，并从它们之间的统计相关性中得出意义。然而，随着时间的推移，社会语言学也相继对客观化提出了质疑。
{"title":"Language is not a data set—Why overcoming ideologies of dataism is more important than ever in the age of AI","authors":"Iker Erdocia, Bettina Migge, Britta Schneider","doi":"10.1111/josl.12680","DOIUrl":"https://doi.org/10.1111/josl.12680","url":null,"abstract":"<p>Helen Kelly-Holmes’ call to explore the implications for sociolinguistics arising from the increased commercially driven digitalization of society is very timely. Like Kelly-Holmes, we share the view that the growing prevalence of online and artificial intelligence (AI) technologies in all aspects of our lives requires a critical assessment of assumptions, approaches, and practices that have grounded sociolinguistic research since its inception. While our discussion confirms Helen's observations, we also urge the development of a general critical attitude toward understanding language as digital data. The starting point for our argument is Helen's claim that there is an erasure of “authentic” languages from public digital spaces, “making it more difficult to gather data on real usage because it would be necessary to rely on public areas and/or negotiate access to these private spaces” (p. 5). For us, her observation brings to the fore that treating language as data has always been problematic. We want to raise two issues: the general epistemological limitations of using digital user data as a representation of language and community, and the consequent need for methods that take seriously the study of language in its social, political, and technological context. We suggest ethnography as a method for understanding what speakers actually do, and an opening of language research to also consider the workings and socio-political embeddings of digital and generative AI language technologies. Our discussion is in the spirit of a joint fruitful and constructive debate.</p><p>Let us start with a general critique of approaching language as “data” that correlates with social groups, which is so far a neglected aspect in the debates surrounding language, sociolinguistics, and AI. Historically, this discussion links to the colonial backgrounds of Western science and linguistics specifically. Colonial or missionary linguistic research (e.g., Deumert & Storch, <span>2020</span>; Errington, <span>2008</span>) demonstrates that dominant Western epistemologies of language and research methods in linguistics were shaped during the period of European colonialism. An important legacy of European colonialism is that it “sought to fundamentally change and reorganize the social and economic order of the societies it colonized, as opposed to satisfy itself with extracting tribute” (Couldry & Mejias, <span>2019</span>, p. 70). Part of this endeavor involved language “development” activities aimed at the goal of Bible translation and turning the colonized into Christian disciples. This was based on constructions of language that are still dominant today. They developed on the grounds of “collecting data” (in colonial times, often from single speakers) and then transforming the human capacity of embodied, interactive and collaborative meaning-making into word lists, grammar books, or dictionaries (e.g., Deumert & Storch, <span>2020</span>; Gal & Irvine, ","PeriodicalId":51486,"journal":{"name":"Journal of Sociolinguistics","volume":"28 5","pages":"20-25"},"PeriodicalIF":1.5,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/josl.12680","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685266","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"(Socio)linguistics and generative AI: Taking the reins as researchers and steering its use toward ethical outcomes","authors":"Matt Kessler, J. Elliott Casal","doi":"10.1111/josl.12682","DOIUrl":"https://doi.org/10.1111/josl.12682","url":null,"abstract":"","PeriodicalId":51486,"journal":{"name":"Journal of Sociolinguistics","volume":"28 5","pages":"31-34"},"PeriodicalIF":1.5,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685277","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Fairness, Relationship, and Identity Construction in Human–AI Interaction","authors":"Jie Dong","doi":"10.1111/josl.12687","DOIUrl":"https://doi.org/10.1111/josl.12687","url":null,"abstract":"","PeriodicalId":51486,"journal":{"name":"Journal of Sociolinguistics","volume":"28 5","pages":"35-37"},"PeriodicalIF":1.5,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Claiming the research expertise on human–GenAI interaction for sociolinguistics","authors":"Kok-Sing Tang","doi":"10.1111/josl.12683","DOIUrl":"https://doi.org/10.1111/josl.12683","url":null,"abstract":"","PeriodicalId":51486,"journal":{"name":"Journal of Sociolinguistics","volume":"28 5","pages":"16-19"},"PeriodicalIF":1.5,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685300","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>Ico Maly is associate professor Digital Culture Studies (Tilburg University, The Netherlands).</p><p>In her opening essay, Hellen Kelly-Holmes asks herself and us ‘how Artificial intelligence will change the way that sociolinguists carry out research’. Instead of giving a clear-cut answer to that question, I would like to take one step back. Before we can think about the concrete ways sociolinguists can use artificial intelligence (AI), it would not be a luxury to first have a sociolinguistic theory on AI. AI is not a neutral tool, it has its own epistemology, produces specific discourses and changes sociolinguistic environments. I do not pretend to have such a full-blown sociolinguistic theory of AI, but I would like to use this opportunity to give a first preliminary sketch of what such a sociolinguistic theorization of AI could look like.</p><p>Starting with the latter, it strikes me how Kelly-Holmes downplays her own work and states that ‘the writing (of ChatGPT) is substantially more correct than my own rambling’ (Kelly-Holmes, 2024). She is clearly not alone in such an assessment of AI. Most users of ChatGPT are equally impressed. It explains the success of the app among our students, and the world at large. By February 2023, the app had 100 million people using it on a weekly basis. And in 2024, that number would rise to 180 million. ChatGPT is now so omnipresent that we have to understand it as a <i>cultural force</i>.</p><p>The discourses ChatGPT produces are being used in a vast number of fields: journalism, law, academia, marketing, politics and digital culture in general. And more, the app is now also embedded in social media like Instagram. Other companies have their own LLMs implemented in search engines, smartphones and social media platforms. AI generates language and is used to moderate language, to help you search, to give you a more personalized digital experience and much more. AI has become a central social structure (re)producing and policing language. And in that sense it gives direction to discourse and culture.</p><p>It is exactly this success that warrants sociolinguistic attention as it has effects on individuals, society and language. On the most micro-level, understanding the relation between AI-produced language and society warrants studying it as interaction. When we do that, we see that users are entering a specific type of communicative relation with specific communicative norms. One entity—the human—is taking up the role of the one asking for information, placing the other—the AI—system in a position of knowledge. This framing of the AI bot as the producer of knowledge is a cultural format. It is steered by the example prompts on the ChatGPT website, but also by the many social media pages and YouTube videos that are dedicated to developing the ‘correct prompts’. The other side of the interaction—the chatbot—is programmed to respond in particular ways. This specifically programmed relation is inherent in the d
{"title":"AI, power and sociolinguistics","authors":"Ico Maly","doi":"10.1111/josl.12681","DOIUrl":"https://doi.org/10.1111/josl.12681","url":null,"abstract":"<p>Ico Maly is associate professor Digital Culture Studies (Tilburg University, The Netherlands).</p><p>In her opening essay, Hellen Kelly-Holmes asks herself and us ‘how Artificial intelligence will change the way that sociolinguists carry out research’. Instead of giving a clear-cut answer to that question, I would like to take one step back. Before we can think about the concrete ways sociolinguists can use artificial intelligence (AI), it would not be a luxury to first have a sociolinguistic theory on AI. AI is not a neutral tool, it has its own epistemology, produces specific discourses and changes sociolinguistic environments. I do not pretend to have such a full-blown sociolinguistic theory of AI, but I would like to use this opportunity to give a first preliminary sketch of what such a sociolinguistic theorization of AI could look like.</p><p>Starting with the latter, it strikes me how Kelly-Holmes downplays her own work and states that ‘the writing (of ChatGPT) is substantially more correct than my own rambling’ (Kelly-Holmes, 2024). She is clearly not alone in such an assessment of AI. Most users of ChatGPT are equally impressed. It explains the success of the app among our students, and the world at large. By February 2023, the app had 100 million people using it on a weekly basis. And in 2024, that number would rise to 180 million. ChatGPT is now so omnipresent that we have to understand it as a <i>cultural force</i>.</p><p>The discourses ChatGPT produces are being used in a vast number of fields: journalism, law, academia, marketing, politics and digital culture in general. And more, the app is now also embedded in social media like Instagram. Other companies have their own LLMs implemented in search engines, smartphones and social media platforms. AI generates language and is used to moderate language, to help you search, to give you a more personalized digital experience and much more. AI has become a central social structure (re)producing and policing language. And in that sense it gives direction to discourse and culture.</p><p>It is exactly this success that warrants sociolinguistic attention as it has effects on individuals, society and language. On the most micro-level, understanding the relation between AI-produced language and society warrants studying it as interaction. When we do that, we see that users are entering a specific type of communicative relation with specific communicative norms. One entity—the human—is taking up the role of the one asking for information, placing the other—the AI—system in a position of knowledge. This framing of the AI bot as the producer of knowledge is a cultural format. It is steered by the example prompts on the ChatGPT website, but also by the many social media pages and YouTube videos that are dedicated to developing the ‘correct prompts’. The other side of the interaction—the chatbot—is programmed to respond in particular ways. This specifically programmed relation is inherent in the d","PeriodicalId":51486,"journal":{"name":"Journal of Sociolinguistics","volume":"28 5","pages":"11-15"},"PeriodicalIF":1.5,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/josl.12681","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685299","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Reviewing and Rebuilding Objects of Inquiry","authors":"Inês Signorini","doi":"10.1111/josl.12686","DOIUrl":"https://doi.org/10.1111/josl.12686","url":null,"abstract":"","PeriodicalId":51486,"journal":{"name":"Journal of Sociolinguistics","volume":"28 5","pages":"49-51"},"PeriodicalIF":1.5,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691211","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
<p>Artificial intelligence (AI) is likely to have a substantial impact on the field of sociolinguistics. AI has the potential to change the landscape of sociolinguistic research in a number of ways. For example, AI tools can assist sociolinguists in analyzing large volumes of social media data to study language variation, linguistic trends, and changes in language use over time. Sentiment analysis and topic modeling algorithms can reveal insights into societal attitudes and language dynamics, helping us to study language ideologies.</p><p>AI-powered speech recognition technologies can aid in the automatic identification and analysis of dialects and accents. AI can streamline the process of conducting large-scale surveys and collecting sociolinguistic data. Chatbots or automated interview tools can be employed to gather responses from diverse populations, facilitating more comprehensive studies of language variation.</p><p>AI can assist in the analysis of linguistic markers related to identity and representation in texts and speech. This includes studying how language is used to construct and express social identities, such as gender, ethnicity, or socioeconomic status. AI algorithms can help sociolinguists analyze social networks and communities based on linguistic interactions. This can provide insights into how language is used within specific social groups and how linguistic patterns contribute to the formation of social networks.</p><p>AI tools can support ethnographic research by automating certain aspects of data analysis. For example, natural language processing algorithms can assist in categorizing and extracting themes from qualitative data, making the analysis process more efficient. AI can contribute to the analysis of language policies and their impact on society. This includes assessing the effects of language planning initiatives on linguistic diversity, language maintenance, and language shift within communities.</p><p>Sociolinguists can use AI to conduct digital ethnography by examining online communities, forums, and virtual spaces. This allows researchers to explore how language is used in digital environments, contributing to a deeper understanding of online sociolinguistics. Collaboration between sociolinguists and computational linguists can lead to the development of AI tools specifically tailored for sociolinguistic research, combining linguistic expertise with computational methods.</p><p>Sociolinguists will need to be mindful of biases in AI models and algorithms. Ensuring fairness and addressing biases is crucial, especially when studying sociolinguistic phenomena that are sensitive to issues such as race, gender, or socioeconomic status. While AI offers exciting possibilities for advancing sociolinguistic research, ethical considerations and the importance of human interpretation and context cannot be understated. Sociolinguists will continue to play a critical role in guiding and interpreting AI-driven analyses to ensu
人工智能（AI）可能会对社会语言学领域产生重大影响。人工智能有可能以多种方式改变社会语言学研究的格局。例如，人工智能工具可以帮助社会语言学家分析大量社交媒体数据，研究语言变异、语言趋势和语言使用随时间的变化。情感分析和话题建模算法可以揭示社会态度和语言动态，帮助我们研究语言意识形态。人工智能驱动的语音识别技术可以帮助自动识别和分析方言和口音。人工智能可以简化开展大规模调查和收集社会语言数据的过程。可以利用聊天机器人或自动访谈工具来收集不同人群的回答，从而促进对语言变异进行更全面的研究。人工智能可以帮助分析文本和语音中与身份和代表性相关的语言标记。这包括研究语言如何用于构建和表达社会身份，如性别、种族或社会经济地位。人工智能算法可以帮助社会语言学家分析基于语言互动的社会网络和社区。人工智能工具可以通过自动完成数据分析的某些方面来支持人种学研究。例如，自然语言处理算法可以帮助从定性数据中分类和提取主题，从而提高分析过程的效率。人工智能有助于分析语言政策及其对社会的影响。社会语言学家可以利用人工智能对在线社区、论坛和虚拟空间进行研究，从而开展数字人种学研究。社会语言学家可以利用人工智能对在线社区、论坛和虚拟空间进行研究，从而开展数字人种学研究。研究人员可以借此探索数字环境中的语言使用方式，从而加深对在线社会语言学的理解。社会语言学家与计算语言学家之间的合作可以开发出专门为社会语言学研究定制的人工智能工具，将语言学专业知识与计算方法相结合。确保公平和消除偏见至关重要，尤其是在研究对种族、性别或社会经济地位等问题敏感的社会语言现象时。虽然人工智能为推进社会语言学研究提供了令人兴奋的可能性，但伦理方面的考虑以及人类解释和语境的重要性也不容低估。社会语言学家将继续在指导和解释人工智能驱动的分析方面发挥关键作用，以确保研究保持伦理上的合理性和文化上的敏感性。"这六个介绍性段落，包括反思和批判部分，你可能猜到了，也可能没有猜到，它们不是我自己对这个主题的研究成果，而是 ChatGPT 在回答我的问题 "人工智能将如何改变社会语言学家开展研究的方式？"时所作。这篇文章比我自己的胡言乱语要正确得多，我故意没有将它缩进，也没有将它与本段开始的我 "自己 "的文章区分开来。人工智能预测，人工智能将给我们的工作方式带来优势和挑战，这并不奇怪。优势一般体现在数据收集和分析工作、收集和处理大量数据的能力，以及将这些数据更广泛地关联起来的能力，而随之而来的劣势则是算法模型中固有的偏差（如 "自动 "方言和口音识别），以及社会语言学研究和分析中人机互动和合作所面临的伦理挑战。令人欣慰的是，人工智能似乎还预言，在这个光明的新未来，仍然需要人类社会语言学家的干预--指导。不到十年前，但就人工智能及其发展而言，已故的 Jan Blommaert 曾描述过社会语言学远离已知世界的过程，其形式是"............"，认为这种转变需要重新思考 "我们所假定的关于社会语言的许多自然的、原始的和常识性的东西需要修正、重新思考和发展"。"(2017, p.
{"title":"Artificial intelligence and the future of our sociolinguistic work","authors":"Helen Kelly-Holmes","doi":"10.1111/josl.12678","DOIUrl":"https://doi.org/10.1111/josl.12678","url":null,"abstract":"<p>Artificial intelligence (AI) is likely to have a substantial impact on the field of sociolinguistics. AI has the potential to change the landscape of sociolinguistic research in a number of ways. For example, AI tools can assist sociolinguists in analyzing large volumes of social media data to study language variation, linguistic trends, and changes in language use over time. Sentiment analysis and topic modeling algorithms can reveal insights into societal attitudes and language dynamics, helping us to study language ideologies.</p><p>AI-powered speech recognition technologies can aid in the automatic identification and analysis of dialects and accents. AI can streamline the process of conducting large-scale surveys and collecting sociolinguistic data. Chatbots or automated interview tools can be employed to gather responses from diverse populations, facilitating more comprehensive studies of language variation.</p><p>AI can assist in the analysis of linguistic markers related to identity and representation in texts and speech. This includes studying how language is used to construct and express social identities, such as gender, ethnicity, or socioeconomic status. AI algorithms can help sociolinguists analyze social networks and communities based on linguistic interactions. This can provide insights into how language is used within specific social groups and how linguistic patterns contribute to the formation of social networks.</p><p>AI tools can support ethnographic research by automating certain aspects of data analysis. For example, natural language processing algorithms can assist in categorizing and extracting themes from qualitative data, making the analysis process more efficient. AI can contribute to the analysis of language policies and their impact on society. This includes assessing the effects of language planning initiatives on linguistic diversity, language maintenance, and language shift within communities.</p><p>Sociolinguists can use AI to conduct digital ethnography by examining online communities, forums, and virtual spaces. This allows researchers to explore how language is used in digital environments, contributing to a deeper understanding of online sociolinguistics. Collaboration between sociolinguists and computational linguists can lead to the development of AI tools specifically tailored for sociolinguistic research, combining linguistic expertise with computational methods.</p><p>Sociolinguists will need to be mindful of biases in AI models and algorithms. Ensuring fairness and addressing biases is crucial, especially when studying sociolinguistic phenomena that are sensitive to issues such as race, gender, or socioeconomic status. While AI offers exciting possibilities for advancing sociolinguistic research, ethical considerations and the importance of human interpretation and context cannot be understated. Sociolinguists will continue to play a critical role in guiding and interpreting AI-driven analyses to ensu","PeriodicalId":51486,"journal":{"name":"Journal of Sociolinguistics","volume":"28 5","pages":"3-10"},"PeriodicalIF":1.5,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/josl.12678","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142691209","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Machines built out of other people's words: Comment on Helen Kelly-Holmes’ discussion article","authors":"Ilana Gershon, Courtney Handman","doi":"10.1111/josl.12684","DOIUrl":"https://doi.org/10.1111/josl.12684","url":null,"abstract":"","PeriodicalId":51486,"journal":{"name":"Journal of Sociolinguistics","volume":"28 5","pages":"44-48"},"PeriodicalIF":1.5,"publicationDate":"2024-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142685298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"文学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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