Magister Artium - MA / The study explores the negotiation of identities through linguistic innovations such as codeswitching, code-mixing and differing language choices in different domains of home and school in Belhar. The focus is to examine how languages are used to negotiate class, age, generational, socio-economic, etc differences in selected schools and homes in the community of Belhar.
The specific study objectives include the following: 1. To find out the linguistic options and identities (including hybrid identities), that are available to the Belhar community. 2. To explore how Afrikaans and English (and other languages) are used as linguistic resources in the community of Belhar. The Belvue Primary school was used as a vehicle to gain access to the families in Belhar which were used as case studies. The data was collected by observing learners in the classroom, interviewing educators, interviewing parents and observing linguistic practices in the homes/families of selected learners. Using poststructuralist coupled with the social constructionist approach the study is a clear departure from studies and paradigms current in vogue in South Africa, which have linked language and ethnic identity in unambiguous ways. These paradigms also see ethnic identity as fixed and communities as homogenous and language as having a one-to-one correlation with identity.
However, these studies do not consider that identities are constructed and negotiated during interaction with others. In this regard it was found that individuals in the community of Belhar constantly construct and negotiate identity using language as central to the identity behaviour. Thus ultimately their language and identity cannot be described as pro-English or pro-
Afrikaans.
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:uwc/oai:etd.uwc.ac.za:11394/2727 |
| Date | January 2009 |
| Creators | Warner, Faika |
| Contributors | Banda, Felix |
| Publisher | UWC |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
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