Alliteration in (Balto-) Finnic Languages

Abstract

This chapter offers an overview of alliteration in Finnic cultures. The focus is on oral rather than written traditions and particularly on two poetic systems which are generally considered to have roots in the vernacular culture going back more than 1000 years. The first of these is the trochaic tetrameter associated with oral epic, lyric and incantation, and which is displayed in the national epics generated for Finnic cultures in the nineteenth century. The second is what we refer to as poetry of ‘unfixed’ meters, associated with traditional laments and yoiks. Following a general introduction to Finnic languages and alliteration, each poetic system is surveyed in turn, beginning in the northern regions near the White Sea, where Finnic groups were in close contact with the Sámi, passing by Lake Ladoga and Lake Olonets, around the Gulf of Finland into Ingria, and on through Estonia until reaching the Balts of Latvia. The orientation of this survey is to provide an accessible synthesised overview of alliteration in Finnic cultures with two objectives. The first is to provide an overview of alliteration as a phenomenon in Finnic cultures generally (as opposed to generalising a localised phenomenon as ‘Finnic’) through examples of vernacular poetic systems common across Finnic cultures. The second is to provide a framework which facilitates approaching alliteration among different Finnic groups or in different regions, and understanding them in relation to one another and in relation to Finnic poetics as a widespread phenomenon. The chapter concludes with an overview which briefly addresses the implications of the survey regarding the evolution of uses of alliteration and affiliated poetic systems within a region, language or cultural group, and the relationship of these processes of evolution to patterns of immediate contact with other groups. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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