Results 191 to 200 of about 2,970 (251)
Orientation towards the vernacular and style-shifting as language behaviours in speech of first-generation Polish migrant communities speaking Norwegian in Norway. [PDF]
Malarski K +4 more
europepmc +1 more source
Abstract This article examines how openness is interpreted in human–LLM interaction through an ethnographic study of a robotics experiment. Focusing on an episode in which a robot produced an unexpected utterance, I analyze how engineers classify the output as inconsequential noise.
Raffaele Andrea Buono
wiley +1 more source
Ethical Practices for Gaining Access for Research with Deaf Communities in South Africa: A Case of South African Sign Language. [PDF]
Sibanda P, Loth CR, Siyavoshi S.
europepmc +1 more source
Palatal violence: Sonic gatekeeping and enemy‐making in wartime Ukraine
Abstract Within 2 days of Russia's full‐scale invasion of Ukraine, a single word—palianytsia, a Ukrainian round loaf—became a phonetic test separating Ukrainians from Russians. The article traces how a culinary term was enregistered as a wartime shibboleth that structurally marks the very citizens it was meant to protect as enemies.
Anatoli Ulyanov
wiley +1 more source
Minority identity and social structures shape diffusion dynamics of minority languages: a combined macro and micro approach. [PDF]
Gao Y, Liu W.
europepmc +1 more source
Computing consensus: Language ideological work in LLM‐assisted deliberative democracy
Abstract This article examines how the digital platform Polis and its experiments with large language models (LLMs) reconfigure democratic participation through a particular vision of consensus. Through analysis of media coverage of Polis and developers' public discussions of the platform, I argue that developers reframe consensus from a discursive ...
Janet E. Connor
wiley +1 more source
It's Not Written All Over My Face: Constructing Chronic Pain as Invisible in Pain Clinic Consultations and Interviews. [PDF]
Declercq J.
europepmc +1 more source
ABSTRACT Machine learning (ML) algorithms have been increasingly used to predict learning disability (LD) risk across various disciplines, but the effectiveness of different algorithms remains unclear. We summarize the literature on ML applications for the identification and classification of LDs using behavioral (e.g., phoneme manipulation and sound ...
Yusra Ahmed +4 more
wiley +1 more source

