Expansion by migration and diffusion by contact is a source to the global diversity of linguistic nominal categorization systems

Abstract

Languages of diverse structures and different families tend to share common patterns if they are spoken in geographic proximity. This convergence is often explained by horizontal diffusibility, which is typically ascribed to language contact. In such a scenario, speakers of two or more languages interact and influence each other’s languages, and in this interaction, more grammaticalized features tend to be more resistant to diffusion compared to features of more lexical content. An alternative explanation is vertical heritability: languages in proximity often share genealogical descent. Here, we suggest that the geographic distribution of features globally can be explained by two major pathways, which are generally not distinguished within quantitative typological models: feature diffusion and language expansion. The first pathway corresponds to the contact scenario described above, while the second occurs when speakers of genetically related languages migrate. We take the worldwide distribution of nominal classification systems (grammatical gender, noun class, and classifier) as a case study to show that more grammaticalized systems, such as gender, and less grammaticalized systems, such as classifiers, are almost equally widespread, but the former spread more by language expansion historically, whereas the latter spread more by feature diffusion. Our results indicate that quantitative models measuring the areal diffusibility and stability of linguistic features are likely to be affected by language expansion that occurs by historical coincidence. We anticipate that our findings will support studies of language diversity in a more sophisticated way, with relevance to other parts of language, such as phonology.

Vocal iconicity in nominal classification

“…The final sample resulted in 344 languages, of which 210 (61.1%) had nonagreeing systems (126 language families) and 151 (43.9%) had agreeing systems (123 language families), aligning rather closely with global distribution between the two main system types: 57.9% non-agreeing and 49.5% agreeing (Allassonnière-Tang et al, 2021). The same language could be included in both the agreeing nominal classification device and non-agreeing nominal classification device samples if it had clearly parallel but unrelated systems.…”

“…This means that iconic signals are very useful for a subset of the linguistic system, but other, noniconic, mappings between signals and meanings are required for providing the remainder of the lexicon with enough labels. Lastly, it should be mentioned that a sizable share of the world's languages completely lacks nominal classification -46.6%, according to the largest available database (Allassonnière-Tang et al, 2021), suggesting that the categorization of nouns does not seem to have any intrinsic value. However, if a language uses a nominal classification system, it ought to be equally communicatively efficient to either rely on semantic transparency, through iconicity, or formal predictability, through agreement.…”

Introduction: Graphemic classifiers in complex script systems

“…Later, the role of classifiers as "referent tracking" devices was established by Croft (1994). The first two decades of the 20th and the beginning of the 21st century saw lively discussions of pronounced classifiers in various languages and theoretical analyses of classifiers' sorts in different cultures and languages (e.g., Aikhenvald, 2000Aikhenvald, , 2021Allassonnière-Tang et al, 2021;Bauer, 2017;Bisang, 2017;Bisang and Wu, 2018;Contini-Morava and Kilarski, 2013;Grinevald, 2004Grinevald, , 2015Senft, 2000), as well as some assessments of their cognitive role across the world's languages (Imai and Saalbach, 2010;Saalbach and Imai, 2007).…”

A network analysis of the semantic evolution of ‘fruit’ and ‘stone’ in Tibeto-Burman languages

“…One remaining issue that is not fully addressed in the paper is the role of language contact in the semantic evolution from nouns to 3D-classifiers. It is generally agreed that classifiers are readily to be borrowed across languages (Greenhill et al 2017;Allassonnière-Tang et al 2021;Her & Li 2023). Indeed, contact-induced classifier borrowings are common among TB languages.…”