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• "Context Shift, Word Drift: The Meaning Transference of the Word Prèet in Thai Society." Kumhaeng, Korrakot et al. Humanities and Social Sciences Communications 12, no. 1 (July 8, 2025): 1050. https://www.nature.com/articles/s41599-025-05454-y.

This research investigates the semantic change and conceptual metaphor of the Thai word prèet (/เปรต/), which originates from the Pali-Sanskrit term meaning “departed.” The primary objective is to explore how the term’s meaning has shifted in contemporary Thai society, where it is now used pejoratively to criticize behaviors such as excessive greed, gluttony, immorality, and social deviance. Data for this study are drawn from both historical texts, particularly the Traibhumi Phra Ruang (a prominent Thai Buddhist text from the 14th-century Sukhothai period), and modern Thai linguistic usage. The analysis employs conceptual metaphor theory, focusing on metaphors like SOCIAL DEVIANCE IS MONSTROSITY, MORAL FAILURE IS DEGRADATION, GREED IS HUNGER, and SPIRITUAL LIMINALITY IS MONSTROSITY. to understand how these shifts reflect changing cultural and societal values. Additionally, Impoliteness Theory is applied to examine how prèet functions as a linguistic tool for social critique. Findings show that the semantic evolution of prèet reveals an intricate relationship between language, culture, and metaphor, as it transitions from a religious concept to a vehicle for social commentary. The implications of this study highlight the dynamic nature of language in reflecting societal shifts.

• "When Dialects Collide: How Socioeconomic Mixing Affects Language Use." Louf, Thomas et al. EPJ Data Science 14, no. 1 (July 10, 2025): 47. https://epjdatascience.springeropen.com/articles/10.1140/epjds/s13688-025-00563-9.

The socioeconomic background of people and how they use standard forms of language are not independent, as demonstrated in various sociolinguistic studies. However, the extent to which these correlations may be influenced by the mixing of people from different socioeconomic classes remains relatively unexplored from a quantitative perspective. In this work we leverage geotagged tweets and transferable computational methods to map deviations from standard English across eight UK metropolitan areas. We combine these data with high-resolution income maps to assign a proxy socioeconomic indicator to home-located users. Strikingly, we find a consistent pattern suggesting that the more different socioeconomic classes mix, the less interdependent the frequency of their departures from standard grammar and their income become. Further, we propose an agent-based model of linguistic variety adoption that sheds light on the mechanisms that produce the observations seen in the data.

• "Re-Examining Second Language Acquisition of English Reflexives: New Evidence for Lexical Learning Driven Process and against First Language Transfer." Zeng, Li et al. Humanities and Social Sciences Communications 12, no. 1 (July 9, 2025): 1063. https://www.nature.com/articles/s41599-025-05466-8.

This study re-examines second language (L2) acquisition of English reflexives by testing 98 first language (L1)-Chinese learners of L2 English with different proficiency levels and 12 native English speakers as controls. Using a truth-value judgment task, we systematically tapped the learners’ judgments of various types of antecedents including long-distance objects. The results show that L2 English learners’ errors in referring English reflexives to long-distance antecedents cannot be due to L1 transfer of Chinese reflexive referential pattern. Instead, these errors align with those documented in the literature on native English children’s acquisition of reflexives. Moreover, as L1-Chinese learners’ English proficiency improved, most of them unlearned the errors, and performed similarly to native English adult controls. This developmental trajectory recapitulates the pattern seen in native English children’s acquisition of reflexives. These findings cast doubt on the view of L1 Chinese transfer and provide support for the Lexical Learning Hypothesis.

• "Metaphor Interpretation in Jordanian Arabic, Emirati Arabic and Classical Arabic: Artificial Intelligence vs. Humans." Zibin, Aseel et al. Humanities and Social Sciences Communications 12, no. 1 (July 1, 2025): 942. https://www.nature.com/articles/s41599-025-05282-0.

This study examines how well humans, both Jordanians and Emiratis, and four AI tools—ChatGPT-4, ChatGPT-3.5, Google Gemini, and Ask PDF—can understand metaphors in Classical Arabic (CA) and its everyday forms in Jordanian Arabic (JA) and Emirati Arabic (EA). We tested fifty participants from Jordan and the UAE on their grasp of various colloquial and CA metaphorical expressions. Two distinct tests were employed, each comprising 40 items. Test 1 was administered to Jordanian participants and included 20 metaphorical expressions in Jordanian Arabic and 20 metaphorical expressions in Classical Arabic. Similarly, Test 2 was administered to Emirati participants and contained 20 expressions in Emirati Arabic and 20 expressions in Classical Arabic. The Mann–Whitney U test was employed to evaluate differences in accuracy and interpretation between AI tools and human participants from both regions in the contexts of colloquial and Classical Arabic. The results showed that participants from Jordan had a better understanding than the AI tools, likely due to their strong cultural background. In contrast, the Emirati participants performed similarly to the AI. The AI tools were more effective at interpreting CA metaphors compared to Emirati participants; AI tools are typically trained on diverse datasets and that usually leads to strong performance in interpreting formal or Classical Arabic expressions. These findings emphasize the need for improvements in AI models to boost their language processing abilities, as they often miss the cultural aspects required for accurately interpreting figurative language. This study adds to the ongoing discussion about AI and language interpretation, revealing both the potential and the obstacles AI faces when dealing with culturally rich and context-sensitive language.

Religions, topolects, language learning, AI — linguistics is exciting and ever changing, never boring.

[Thanks to Edward M "Ted" McClure]

July 14, 2025 @ 4:20 pm · Filed by Victor Mair under Dialects, Metaphors, Second language, Semantics, Sociolinguistics

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