The article reports on a study of the feminine gender in a Western dialect of Norway, using a production experiment eliciting the use of indefinite articles, possessives, adjectives, and the definite suffix. The participants (n=64) are of three age groups: children, adolescents, and adults. The results show that while the feminine gender is stable in the language of the adults, it is becoming vulnerable in the language of the children and adolescents. The main tendency is that feminine gender markers are being replaced with masculine markers. I argue that the innovations are interconnected and not random, and that numerous gender cues do not necessarily make the system more stable.

With rising political polarisation, far-right digital spaces have become fertile ground for amplifying xenophobic discourse. Adopting a critical multimodal analytic approach to stance analysis, we examined anti-immigration posts and comments related to the US context in the QAnon+ Telegram channel from January 2021 to October 2022 to show that radicalisation in this context is not merely a matter of extreme opinions, but of the performative multimodal enactment of stance that drives ideological alignment. The anti-immigration discourse is dominated by attitude markers and boosters, while hedging and self-mention are scarce, reflecting a tendency towards affective intensity and ideological closure over deliberation or reflexivity. Emojis, as paralinguistic resources, co-perform stance by amplifying shared outrage and mobilising group alignment. The overall argument is that hate in radical digital publics is enacted through patterned multimodal and performative processes, explaining the mechanisms that make such spaces resilient to rational counter-argument and potent for collective extremism. (Anti-immigration, far-right, multimodal critical discourse analysis, group alignment, performativity, QAnon, stance-taking)

In this work, we use language modeling to investigate the factors that influence insertional code-switching. Code-switching occurs when a speaker alternates between one language variety (the primary language) and another (the secondary language), and is widely observed in multilingual contexts. Recent work has shown that code-switching is often correlated with areas of low predictability in the primary language, but it is unclear whether low primary language predictability only makes the secondary language relatively easier to produce at code-switching points – that is, purely speaker-driven code-switching – or whether code-switching is additionally used by speakers for other purposes, for instance to signal the need for greater attention on the part of listeners. In this paper, we use bilingual Chinese–English online forum posts and transcripts of spontaneous Chinese–English speech to replicate prior findings that low primary language (Chinese) predictability is correlated with insertional switches to the secondary language (English). We then demonstrate that the predictability of the English productions is even lower than that of meaning-equivalent Chinese alternatives, and these are therefore not easier to produce, rejecting the purely speaker-driven theory of code-switching in both writing and speech.

All linguistic research has the potential to reproduce or challenge racial notions.

—Linguistic Society of America Statement on Race (2019)

The LSA Statement on Race stems from a larger conversation around undertheorized treatment of race and ethnicity in linguistics research and practice. In this commentary, we define racial identity and ethnicity and explain their relevance for linguistic research. We discuss considerations that linguistic researchers should take prior to research, during study design, and following research, and we offer specific recommendations when soliciting or using race and ethnicity data. These recommendations aim to help researchers avoid social harm, ensure ethical compliance and research integrity, and improve descriptive accuracy, especially for undersampled groups, by balancing research transparency with generalizability. We consider issues germane to collecting self-disclosures of ethnicity and racial identity in a range of study types spanning several subfields of linguistics. We give concrete examples of questions that may arise in planning studies in computational and corpus-based linguistics, formal linguistics, experimental linguistics, and qualitative linguistics. We speak to ethical considerations, including the importance of using locally constructed labels, analyst positionality, and respect for communities. Our goals are to provide linguistic researchers with a firmer basis for conceptualizing racial identity and ethnicity particularly as pertains to linguistics, and to supply a guide that aids linguists in reflecting on their own study design, positionality, and responsibility to participants and communities.

Inflectional morphology refers to the mapping from grammatical information to surface forms, which are typically realized as morphemes. This mapping often exhibits fusion, where several abstract features are expressed in a single morpheme that cannot be decomposed into meaningful parts. Here, we discuss crosslinguistic generalizations of morphological fusion. We argue that fusion reflects principles of efficient processing, as formalized by the memory–surprisal tradeoff (Hahn, Degen, & Futrell 2021), which is based on information-theoretic models of language processing from psycholinguistics. We first show that the existence of fusion itself can, in some situations, lead communicative codes to be more efficient under our processing model. Particularly, we reveal via simulation that the fusion of highly correlated features is more efficient for processing, whereas agglutination is more efficient when features are less correlated. We next discuss crosslinguistic patterns of fusion in real languages. First, we analyze well-known generalizations about features that are commonly fused across languages (e.g. tense, aspect, and mood), as well as a typological pattern regarding suppletion. In both cases, we find that the universals we study tend to reflect a tendency toward more efficient structure under our model of language processing. Finally, we use paradigm and frequency data from four languages to study informational fusion, a gradable measure of fusion defined in Rathi et al. 2021. We find that informational fusion is higher when features are highly correlated, which suggests that gradable fusion is also influenced by optimization for the memory–surprisal tradeoff.