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The combination of certain linguistic units that recurrently appear in text genres has attracted the attention of many researchers in several domains, as they can provide valuable information about different types of relations. In this paper, the focus is on some of these combinatory units, referred to as Lexico-Syntactic Patterns (LSPs) that provide information about conceptual structures present in ontologies, also called Ontology Design Patterns (ODPs). The final end is to create a repository of LSPs associated to the ontological structures they convey to help novice users in the development of ontologies. In this paper we present the different strategies we have followed to identify LSPs, as well as an excerpt of the repository of LSPs-ODPs that is currently being built.

This collection provides a kaleidoscopic view of a range of identity struggles in the workplace context. It features twenty-two case studies that present an eclectic mix of workplaces in different socio-cultural contexts. They include, among others, household workers in Peru and Hong Kong, female professionals in India and the UK, social workers in Botswana and on Canadian reserves, tourist guides in Europe and construction workers in New Zealand. The volume addresses important questions on professional competence, group membership, (sometimes competing) expectations, and identity boundaries. The chapters establish that identity struggles are a reflection of issues of knowledge, competing norms and attempts for social change.

Während Indien bis vor wenigen Jahren noch vor allem als beliebte Destination für das Outsourcing von Dienstleistungen bekannt war, gewinnen indische Firmen, wie beispielsweise der Konzern Tata oder die Unternehmen im \"indischen Silicon Valley\" in Bangalore, inzwischen international zunehmend an Bedeutung. Doch auch die traditionellen Reichtümer Indiens gelangen zu immer mehr Ansehen. Jahrtausendealte Lehren wie Yoga, Meditationstechniken oder die ayurvedische Heilkunst finden außerhalb Indiens stetig neue Anhänger, insbesondere in den westlichen Ländern. Wenn diese zunehmend wichtige Rolle in der globalisierten Welt im Zusammenhang mit der großen ethnischen, kulturellen und sprachlichen Diversität Indiens betrachtet wird, ergibt sich die Frage, wie sich die Situation der indischen Sprachen im Kontext der politischen, gesellschaftlichen und wirtschaftlichen Entwicklung des Landes verändert. In dieser Arbeit wird die derzeit am weitesten verbreitete indische Sprache, Hindi, betrachtet. Der Fokus liegt dabei auf der Fachkommunikation in Hindi und dem Einfluss von Gesellschaft, Wirtschaft und Politik auf den Status des Hindi im naturwissenschaftlich-technischen Bereich.

Large language models (LLMs) have demonstrated impressive performances in various medical domains, prompting an exploration of their potential utility within the high-demand setting of emergency department (ED) triage. This study evaluated the triage proficiency of different LLMs and ChatGPT, an LLM-based chatbot, compared to professionally trained ED staff and untrained personnel. We further explored whether LLM responses could guide untrained staff in effective triage. This study aimed to assess the efficacy of LLMs and the associated product ChatGPT in ED triage compared to personnel of varying training status and to investigate if the models' responses can enhance the triage proficiency of untrained personnel. A total of 124 anonymized case vignettes were triaged by untrained doctors; different versions of currently available LLMs; ChatGPT; and professionally trained raters, who subsequently agreed on a consensus set according to the Manchester Triage System (MTS). The prototypical vignettes were adapted from cases at a tertiary ED in Germany. The main outcome was the level of agreement between raters' MTS level assignments, measured via quadratic-weighted Cohen κ. The extent of over- and undertriage was also determined. Notably, instances of ChatGPT were prompted using zero-shot approaches without extensive background information on the MTS. The tested LLMs included raw GPT-4, Llama 3 70B, Gemini 1.5, and Mixtral 8x7b. GPT-4-based ChatGPT and untrained doctors showed substantial agreement with the consensus triage of professional raters (κ=mean 0.67, SD 0.037 and κ=mean 0.68, SD 0.056, respectively), significantly exceeding the performance of GPT-3.5-based ChatGPT (κ=mean 0.54, SD 0.024; P<.001). When untrained doctors used this LLM for second-opinion triage, there was a slight but statistically insignificant performance increase (κ=mean 0.70, SD 0.047; P=.97). Other tested LLMs performed similar to or worse than GPT-4-based ChatGPT or showed odd triaging behavior with the used parameters. LLMs and ChatGPT models tended toward overtriage, whereas untrained doctors undertriaged. While LLMs and the LLM-based product ChatGPT do not yet match professionally trained raters, their best models' triage proficiency equals that of untrained ED doctors. In its current form, LLMs or ChatGPT thus did not demonstrate gold-standard performance in ED triage and, in the setting of this study, failed to significantly improve untrained doctors' triage when used as decision support. Notable performance enhancements in newer LLM versions over older ones hint at future improvements with further technological development and specific training.

The introduction of English and other foreign languages as media of instruction, which is generally referred to as content and language integrated learning (CLIL), has transformed the teaching experiences of a large number of educators. Yet their daily struggles and personally ambivalent stances have hardly been examined. This paper addresses this overlooked area of CLIL practice by taking a critical sociolinguistic stance towards language-in-education policy. Drawing on an ethnographic case study, it analyses the on-the-ground implementation of PEP, a government initiative to foster the plurilingualisation of the Catalan education system, in a state secondary school near Barcelona. Through the situated analysis of policy makers’, administrators’, and educators’ actions and discourses, the paper shows how the different groups of actors rationalise their engagement with the programme differently, while still aligning themselves with the official imagination of PEP, and constructing a collective ethos of commitment and hard work to improve the school’s reputation. Three neoliberalised worker subject positions are identified: the entrepreneurial head teacher, who anticipates avenues for school transformation before they are put into place; the activised civil servants , who construct themselves as exemplary moral agents; and the maximally flexible temporary teachers , who live their participation in PEP with anxiety and a sense of burden, but are also aware of the many opportunities PEP offers. This paper contributes situated insights on CLIL implementation and addresses issues of power and inequality overlooked by the dominant paradigms in the field.

This book investigates the impact of language learning and study abroad on the career options and choices of US-based alumni of all ages. International education experiences are shown to exert considerable influence on the aspirations and career paths of individuals, and the long-term benefits are clearly demonstrated in participant narratives.

Hong Kong’s ‘trilingual and biliterate’ language policy (TaB, 三語兩文 ) is almost as old as the special administrative region (SAR) itself. Through free education and language support measures in school, students are expected to be conversant in English and Putonghua in addition to Cantonese, and be able to read and understand written Chinese and English. After being implemented for over two decades, however, there are signs that most students’ language standards in Chinese and English fall short of the TaB target, as measured by the public examination results of successive generations of secondary school leavers. Designed with essentially Cantonese-dominant Hongkongers in mind, the TaB policy consists of many measures, with the ‘medium of instruction streaming policy’ introduced since September 1998 being the most controversial. Driven by the twin principles of monolingual English-medium instruction (EMI) and ‘no language mixing allowed’, secondary schools are divided into two streams. Stringent requirements must be met before a school could claim to be an EMI school. According to this ‘late immersion’ model for students aged 11–12 at secondary level, every year about 30 percent of the primary school leavers are allocated to an EMI school. Following Li ( Multilingual Hong Kong: languages, literacies and identities . Springer, Cham, 2017), this paper will first discuss why the TaB target is such a tall order for Cantonese-dominant students by reviewing the relevant literature along five inter-related dimensions: contrastive linguistics, psycholinguistics, sociolinguistics, cognitive neuroscience, and instructional strategies and bilingual pedagogies. I will then examine the SAR government’s language support measures to assess their effectiveness and explore possible alternatives. The paper will end with a number of recommendations, which together constitute an LPP (language policy and planning) roadmap for improving the chance with which the TaB policy is likely to produce more positive outcomes. (i) To re-prioritize the investment and extent of language support by strengthening the quality of language input at the key stages of learning from age 3–9, which in curriculum terms correspond with K1–P3; (ii) To use Cantonese as the medium of instruction for teaching all subjects except English and Putonghua as separate subjects at preschool (K1–K3, age 3–6); (iii) To explore the possibility of implementing total immersion in Putonghua for three years at lower primary level (P1–P3, age 6–9); (iv) To abandon the ‘maximum exposure, no mixing’ dogma in secondary education and to encourage basic and action research in bilingual pedagogies and instructional strategies informed by Content-and-language integrated learning (CLIL); (v) To attract academically bright and linguistically gifted students to receive professional training and be prepared and committed for a career in language teaching; and (vi) To encourage civil servants of various government offices, schoolteachers, and university staff to initiate a ‘speak English/Putonghua where we can’ campaign. For these policy measures to be successfully implemented and bear fruit, apart from careful planning, there ought to be strong leadership from within the government and the education sector, plus mechanisms for coordinating concerted actions on the part of various groups of stakeholders, notably teachers, school principals, educationalists, and experts of language teaching and learning in academia.

Large language models have received enormous attention recently with some studies demonstrating their potential clinical value, despite not being trained specifically for this domain. We aimed to investigate whether ChatGPT, a language model optimized for dialogue, can answer frequently asked questions about diabetes. We conducted a closed e-survey among employees of a large Danish diabetes center. The study design was inspired by the Turing test and non-inferiority trials. Our survey included ten questions with two answers each. One of these was written by a human expert, while the other was generated by ChatGPT. Participants had the task to identify the ChatGPT-generated answer. Data was analyzed at the question-level using logistic regression with robust variance estimation with clustering at participant level. In secondary analyses, we investigated the effect of participant characteristics on the outcome. A 55% non-inferiority margin was pre-defined based on precision simulations and had been published as part of the study protocol before data collection began. Among 311 invited individuals, 183 participated in the survey (59% response rate). 64% had heard of ChatGPT before, and 19% had tried it. Overall, participants could identify ChatGPT-generated answers 59.5% (95% CI: 57.0, 62.0) of the time, which was outside of the non-inferiority zone. Among participant characteristics, previous ChatGPT use had the strongest association with the outcome (odds ratio: 1.52 (1.16, 2.00), p = 0.003). Previous users answered 67.4% (61.7, 72.7) of the questions correctly, versus non-users’ 57.6% (54.9, 60.3). Participants could distinguish between ChatGPT-generated and human-written answers somewhat better than flipping a fair coin, which was against our initial hypothesis. Rigorously planned studies are needed to elucidate the risks and benefits of integrating such technologies in routine clinical practice.

With globalization and the ever-increasing migration of professionals, issues related to learning an additional language and culture in professional contexts are prominent in many contemporary societies.