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Abstract

Every citizen of Kazakhstan has the right to ethnic self-identification. In line with Brubaker’s (2004) idea of ethnicity being not rigid, but fluid and situational, this study explores the relationship between Kazakh language proficiency and ethnic self-identification among ethnic Russians in Kazakhstan. Understanding the country’s ethnolinguistic dynamics is important, particularly how ethnic Russians navigate their dual identities as a minority in the post-Soviet Kazakhstan. In this study, the role of Kazakh language can be addressed in two different ways: first, as a mother tongue, and second, as a second language. Prior research on language and ethnic identification have mixed results, some show no clear dependence of ethnic self-identification on language competence, others discover a relationship between the language and self-ethnic identity. Drawing from Ethnolinguistic Identity Theory (Giles & Johnson, 1987), which highlights the importance of language as a central marker of group belonging, we argue that ethnic Russians with a higher level of Kazakh language proficiency tend to be less likely to solely self-identify as ethnic Russians. Depending on their language proficiency, they tend to be more bi-ethnic and identify as both Russian and Kazakh at the same time, or even to be more Kazakh than Russian. These patterns also reflect concepts from Bicultural Identity Integration Theory (Benet-Martinez & Haritatos, 2005). It suggests that individuals vary in how they perceive and reconcile multiple cultural identities. Another interesting aspect of this research is the ethnic identity that is stated on national IDs. This formal classification may not always align with individuals’ lived experiences and personal sense of belonging. The fundamental question to ask the respondents is “How would you self-identify ethnically?”. Our second argument is that ethnic Russians who do not have an ethnic identification on their governmental ID relatively have a higher level of Kazakh language proficiency. Ultimately, they are more likely to feel part of the Kazakhstani nation and identify as Kazakhs. This study uses a cross-sectional online survey with a non-probability quota sampling approach. National census data were used to establish the target proportions of ethnic groups, gender, and other demographic indicators. Participants will be recruited through snowball sampling, but recruitment continues until the sample matches the population proportions (e.g., 14.6% ethnic Russians, 71.3% Kazakhs) based on the most recent census data.