The historical nature of language contact between French and Arabic in Algeria has created a sociolinguistic situation in which French is permeated throughout Algerian society. The prevalence and use of spoken French in Algeria by native speakers of Spoken Algerian Arabic has been a topic of interest to researchers of codeswitching since the 1970s. Studies have been conducted on codeswitching in Algerian media such as television, radio, and music.
The hip hop scene has been active in Algeria since the 1980s. Algerian hip hop lyrics contain a multitude of switches into French. This study explores the structural makeup of the codeswitching between French and Spoken Algerian Arabic in Algerian hip hop. These are pattern that have gone heretofore unstudied.
The purpose of this study was to utilize Myers-Scotton's MLF and 4-M models in order to analyze the codeswitching between Spoken Algerian Arabic and French found in the lyrics to the hip hop album Kobay by popular Algerian hip hop artist Lotfi Double Kanon. This study had two goals: the first was to document the structural patterns of the codeswitching found in the data. The second goal was to test Myers-Scotton's models and determine whether the patterns found in the data could be predicted by the MLF and 4-M models.
In order to accomplish these goals, the lyrics to the album were transcribed, translated, coded and analyzed at the level of the complementizer phrase. The principles of the MLF and 4-M models were used as central tool for analysis.
This study demonstrates that the codeswitching found in the lyrics to Kobay follow the principles of the MLF and 4-M models to a great extent. However, three examples of problematic data are presented. This is followed by a discussion on the social and structural implications of these findings.
| Identifer | oai:union.ndltd.org:pdx.edu/oai:pdxscholar.library.pdx.edu:open_access_etds-2630 |
| Date | 26 February 2014 |
| Creators | McLain-Jespersen, Samuel Nickilaus |
| Publisher | PDXScholar |
| Source Sets | Portland State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations and Theses |
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