A Discourse Analysis of Three Past TAM Forms in Vwanji
This paper presents data from Vwanji, an under-documented Bantu language spoken by approximately 28,000 people in southwestern Tanzania. Bantu languages are well known for having multiple degrees of past time reference grammaticalized in their TAM systems, and Vwanji is a good example of such a lang...
| Main Author: | |
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| Format: | Electronic Article |
| Language: | English |
| Check availability: | HBZ Gateway |
| Journals Online & Print: | Drawer...
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| Fernleihe: | Fernleihe für die Fachinformationsdienste |
| Published: |
[publisher not identified]
2020
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| In: |
Studia Orientalia Electronica
Year: 2020, Volume: 8, Issue: 3, Pages: 65-80 |
| Further subjects: | B
Vwanji
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| Online Access: |
Presumably Free Access Volltext (lizenzpflichtig) Volltext (lizenzpflichtig) |
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| 520 | |a This paper presents data from Vwanji, an under-documented Bantu language spoken by approximately 28,000 people in southwestern Tanzania. Bantu languages are well known for having multiple degrees of past time reference grammaticalized in their TAM systems, and Vwanji is a good example of such a language, but one with some interesting typological differences from certain general TAM trends in Bantu languages noted in Nurse (2008). Three past TAM forms, in particular, are the focus of the research: P1 /Anterior SM-VB-ile, P2 SM-a-VB-a, and the Near Past Habitual SM-a-VB-aɣa. The analysis of data from a corpus of narrative and non-narrative texts (both written and oral) reveals that these three TAM forms have multiple discourse functions which do not necessarily follow in expected ways from their places in the TAM system as a whole. Comparing the Vwanji findings with those of neighbouring languages suggests some possible directions in which the verb forms in Vwanji may be changing functionally or being lost. The goal of this investigation is to increase understanding of a typologically interesting language which has not been well described and for which there is very little published data. The paper also shows the importance of taking natural discourse data into account when considering TAM functions in a language. Relying on elicited data alone may hide interesting complexities and variation. | ||
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