Sanzhi–Russian code switching and the Matrix Language Frame model

Abstract

Purpose: The study represents the first attempt to analyze intrasentantial code switching in an indigenous language from the Caucasus (the Nakh- Daghestanian language Sanzhi Dargwa) in contact with Russian. It also tests borrowing/code switching hierarchies that target parts of speech. Methodology: The study applies the Matrix Language Frame model developed by Myers-Scotton to data from Sanzhi. Data and analysis: The analyzed data consist of around 6,000 tokens of natural texts (monologues) produced by six male speakers and recorded in the main settlement of the Sanzhi speech community in Daghestan (Russian Federation). The original data are compared to published data from other languages in contact with Russian. The Sanzhi data are analyzed by means of the Matrix Language Frame model, focusing on intraclausal code switching. Findings: The Sanzhi data can largely be analyzed within the Matrix Language Frame model, confirming thus the ‘Uniform Structure Principle’ posed by Myers-Scotton. However, there are also a few instances of code switching in which embedded language and matrix language cannot be identified, which prevents application of the model. Furthermore, the study replicated findings on borrowing/code switching hierarchies for parts of speech, that is, the preference for insertions of nouns and other parts of speech from the open classes in comparison with the relative scarcity of inserted pronouns or adpositions (closed classes). Originality: This is the first attempt to apply the Matrix Language Frame model to code switching between a Caucasian language and Russian and constitutes a new approach to the study of language contact in the Caucasus and, more generally, to the impact on Russian of minority languages in the Russian Federation. Implications: The results suggest that the Matrix Language Frame model could also be applied to other languages in contact with Russian and with a similar sociolinguistic profile, such as Sanzhi.