The present paper discusses how insights from translanguaging theory and pedagogy can help inform and promote genre pedagogy for teaching business communication courses such as writing and translation. To this end, the first part traces and reviews the developments of genre theory and pedagogy in tandem with translanguaging theory and pedagogy, thus teasing out their similarities and differences in historical roots, core premises, theoretical frameworks, and research methodologies. In light of these integrated accounts, the second part of the paper proposes a genre-based pedagogical framework augmented with key tenets and general principles of translanguaging pedagogy to design and implement task activities and classroom practice in teaching business communication courses in the Greater Bay Area (GBA). The paper then outlines perceivable advantages and potential challenges to the application of this translanguaging-informed genre-based pedagogical approach in curriculum design and professional training practice in superdiverse megapolis regions such as the GBA.

Although emotional or affective working memory (WM) is quite well established in general psychology, not much research has looked into its potential implications for the language sciences and bilingualism and second language acquisition (SLA) research until recently. To fill this gap, this paper aims to propose that WM has not just cognitive implications, but its affective dimension may also make complementary and unique contributions to language and bilingualism/SLA research. Towards this end, we first briefly synthesize the cognitive views of WM conceptions and assessment procedures in the current language sciences and bilingualism/SLA research. Next, we turn to discuss the theoretical models and assumptions of affective WM and explore their theoretical implications for bilingualism/SLA research based on emerging empirical evidence. Then, we propose a conceptual framework integrating cognitive and affective WM perspectives and further provide guidelines for designing affective WM span tasks that can be used in future affective WM–language research, focusing on the construction procedures of several emotion-based affective WM span tasks (e.g., the emotional reading span task, the emotional operation span task, and the emotional symmetry span task) as examples. Overall, we argue that affective feelings are also an integral part of the mental representations held in WM and future research in the language sciences and bilingualism/SLA should incorporate both cognitive and affective WM dimensions.

This special issue sets out to revisit major bilingual and multilingual education policy and planning issues in key cities across the dynamic Greater Bay Area (GBA) of China (including Guangdong, Hong Kong, and Macau), as informed by the emerging insights from translanguaging theory (Li in Appl Linguist 39(1):9–30, 2018). Specifically, contributions are invited from the GBA and beyond to trace the milestone developments of relevant language policy and planning (LPP) initiatives featuring governmental policies on the language of instruction (e.g., English as Medium of Instruction, Chinese as Medium of Instruction) and the daily use of multiple linguistic resources or repertoires (e.g., language learner's L1 or mother tongue) in foreign/second language classrooms and/or in content-based classroom instruction. As such, major papers in the special issue not only set out to provide historical and analytical reviews of these related LPP issues across Hong Kong, Macau, and Guangdong Province but also reflect upon the language use and behaviors of the residents, showcasing their identity and attitudes and ideological stance toward the multiple languages that are being taught or used in daily life. Toward the end, we outline our research agendas for future endeavors in broader domains in the GBA and beyond through the translanguaging analytical tools. Overall, our position is that the GBA presents itself as an evolving, complex, and superdiverse zone of “Translanguaging Spaces” within which key LPP issues may need to be reconceptualized and implemented in such a way so as to fully reflect the dynamic and fluid multilingual, multicultural, and multisemiotic lived reality of the residents in this megapolis region.

The integration of artificial intelligence (AI) in translation assessment represents a significant evolution in the field, transcending traditional human-only scoring approaches. This study specifically examines the role of ChatGPT, a multilingual, transformer-based large language model developed by OpenAI, in the automated evaluation of machine translations between Portuguese and Mandarin. Despite ChatGPT's burgeoning reputation for its advanced Natural Language Processing (NLP) capabilities, research on its application in translation evaluation, particularly for this language pair, remains unexplored. To fill this gap, our research employed three prevalent machine translation tools to translate a set of twenty sentences from Chinese into Portuguese. Translated target text versions provided by professional Chinese-Portuguese translators were also included to estimate if the machine-translated target texts have achieved a certain degree of human parity. We then assessed these translations using both GPT models (ChatGPT 3.5 and ChatGPT 4.0) and five human raters to offer a comprehensive scoring analysis. The study's findings reveal that, particularly ChatGPT 4.0, exhibits substantial promise in evaluating translations across varied text types. However, this potential is tempered by notable inconsistencies and limitations in its performance. Through both quantitative analysis and qualitative insights, this research highlights the synergy between ChatGPT's automated scoring and traditional human assessment. It uncovers some key benefits of this automated approach: (1) exploring viability of automated translation evaluation, particularly in Chinese-Portuguese language pair; (2) fostering critical supplement to human evaluation, and (3) uncovering the astonishing capability of cutting-edge machine translation tools in Chinese-Portuguese language pair. Our findings contribute to a more detailed comprehension of ChatGPT's role in translation assessment and underscore the need for a balanced approach that leverages both human expertise and AI capabilities.

The present study sets out to investigate how multilingual youth perceive and represent their linguistic repertoires. To achieve this goal, we introduced a computer-vision-aided analytical method to deal with the obtained visual data, which comprised digital images of language portraits created by a group of young multilingual speakers. An OpenCV module is used to build and complete the graphic data processing, enabling quantitative evaluations of participants’ colored clusters and linguistic codes that express their language repertoires. In combination with oral narratives provided in their language portraits, the findings demonstrate that Macanese heritage speakers show a higher degree of “scope” than the Chinese mainland sojourners in Macao but a lower degree of “access”. Follow-up interviews further corroborated the self-perceptions of their linguistic resources across different registers. Overall, the computer-vision-aided analysis of language portraits enhances the current understanding of the “scope” and “access” of multilingual repertoires in lived experience.