This study examines relative clauses in the German version of the General Data Protection Regulation (GDPR), using the Chinese translation published by Rui Bai Law Firm as its principal corpus. Guided by Li Kexing’s static equivalence theory, it employs case analysis to investigate the methods used to translate these relative clauses into Chinese. Four recurrent methods are identified: pre-positioning, anaphora, conversion, and fusion. The selection of these methods varies according to the syntactic structure, semantic function, and logical relations of individual relative clauses. These methods help preserve the normative legal meaning and logical relations of the source text while improving the clarity and fluency of the target text and adapting it to the conventions of Chinese legal discourse. Together, they contribute to a relatively stable correspondence between the source and target texts and thus to the realization of static equivalence.
With generative artificial intelligence (AI), AI translation is increasingly used in cross-cultural communication, yet its adaptive mechanisms remain underexplored. Grounded in eco-translatology, this study compares official Japanese translations with AI outputs (DeepSeek, ChatGPT, and Google Translate) for Chinese publicity texts. Findings show distinct adaptive patterns across linguistic, cultural, and communicative dimensions. Linguistically, AI achieves formal alignment and semantic transfer but struggles with implicit meanings. Culturally, it unpacks dense symbols, weakening symbolic richness. Communicatively, it neutralizes rhetoric and emotion, reducing persuasive intensity. Overall, AI translation reflects an algorithmic logic of formal alignment, semantic explicitation, and functional simplification—not simply quality deficiency. This study contributes by highlighting structural imbalance among dimensions and advancing understanding of AI-mediated cross-cultural translation.
This paper examines the translation strategies employed by Hsien-yung Pai in his self-translation of Taipei People through the lens of George Steiner’s fourfold hermeneutic motion—trust, aggression, incorporation, and compensation. As a bilingual author-translator, Pai occupies a unique position in which authorial intent and translatorial interpretation converge. Drawing on textual evidence from the Chinese-English bilingual edition and existing scholarship, this study argues that Pai’s self-translation demonstrates distinctive advantages in comprehension and expression, while still confronting the inevitable challenges of cross-cultural and cross-linguistic mediation. The analysis reveals that Pai adopts a predominantly reader-oriented strategy, flexibly deploying literal translation, free translation, substitution, addition, and annotation to achieve functional equivalence and cultural accessibility for English-speaking readers.
Creative language use on social media is easily deemed mere entertainment, while related phonological wordplay is often equated with humour. Researchers commonly investigate linguistic items associated with social media users, with limited attention to related sound-based creativity. This study examines Chidera Madu’s TikTok discourse to identify and classify his phonological wordplay strategies and determine how they contribute to meaning-making and perform communicative functions. Framed in Carter’s Framework of Linguistic Creativity, 47 extracts of Chidera Madu’s phonological wordplay are purposively analysed using a qualitative descriptive-interpretive design. Findings reveal four phonological strategies employed in Chidera’s TikTok discourse, including resegmentation, homophones, phonemic substitution, and rhyme-based wordplay. The resegmentation occurs as word-to-phrase, phrase-to-word, and phrase-to-phrase, while the rhymes are masculine, feminine, and internal rhymes. The study found that Chidera’s wordplays contribute to meaning-making through reinterpreting and shifting meanings, and performs communicative functions by serving various discourse functions, including humour, praise, satire, indirect communication, affirmation, emphasis, and social commentary. The findings are relevant to applied linguists and researchers on digital discourse, linguistic creativity, and Nigerian multilingual communication. The researcher recommends examining informal social-media discourse, such as TikTok, as a resource for linguistic innovation.
Artificial intelligence (AI) is reshaping university English teaching. Teachers’ AI literacy is a key variable determining whether technology can be transformed into teaching effectiveness. Grounded in the Technological Pedagogical Content Knowledge (TPACK) framework and considering university English as both humanistic and instrumental, this study constructs a three-dimensional framework of university English teachers’ AI literacy: knowledge foundation (AI-TK, AI-TCK, and AI-TPK), core competencies (AI-TPACK integrated application, teaching evaluation, and ethical judgment), and dispositional attitudes (openness, critical thinking, and willingness to innovate). Data were collected from 50 open-ended questionnaires and 15 semi-structured interviews with English teachers at a science and engineering university. Nvivo 11.0 was used for open, axial, and selective coding. Findings reveal superficial technological cognition, fragmented integrated application, weak ethical judgment, and three tensions: technological efficiency versus humanistic warmth, data-driven instruction versus professional judgment, and tool use versus role identity. The study proposes a multi-stakeholder model coordinated among government, universities, society, and individuals, with teaching-research pathways of knowledge supplementation, competence forging, and awareness awakening.
