On the typology of locative predication in Samoyedic languages

Within the given paper, I investigate the patterns of the linguistic expression of locative predication (formalized as “X BE.AT Y”) in the Samoyedic languages, taking into account the two major typological approaches of Stassen (1997) and Ameka & Levinson (2007). The following patterns are shown...

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Bibliographic Details
Main Author: Chris Lasse Däbritz
Format: Book Part
Language:unknown
Published: Helsingin yliopiston kirjasto 2022
Subjects:
Online Access:https://zenodo.org/record/8233797
https://doi.org/10.31885/9789515180858
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Summary:Within the given paper, I investigate the patterns of the linguistic expression of locative predication (formalized as “X BE.AT Y”) in the Samoyedic languages, taking into account the two major typological approaches of Stassen (1997) and Ameka & Levinson (2007). The following patterns are shown: The encoding of the theme (unmarked subject) and the location (spatial adverbial included in the predicate) does not differ across the Samoyedic languages, but the linking element: In affirmative locative clauses, most Samoyedic languages exhibit a copula verb, which appears in predicate nominals/adjectives as well. The major exception from this pattern is the Forest Enets locative copula verb ŋa- ‘to be at’, which I discuss in more detail since its locative semantics appear to be a recent functionally motivated development. In negative locative clauses, in turn, negative existential verbs are used in all Samoyedic languages. Consequently, Samoyedic languages show a polarity split in the encoding of locative predication. Arguing that a locative interpretation of the successor forms of the Proto-Samoyedic copula verb is not felicitous from a synchronic point of view, I discuss the typological approaches of Stassen (1997) as well as Ameka & Levinson (2007). Finally, I present a first attempt at typological classification of locative predication, which is based on the analysis of the Samoyedic languages but might be validated by taking into account data from a much larger sample of languages.