Creating awareness of Inclusive Citizenship Education to address gangsterism in a community

Abstract

Gangsterism is considered a major issue in the South African context. Various studies have been done to address this phenomenon in the Cape Flats; however, no studies have been done in small rural areas regarding this phenomenon. It is thus essential to provide a platform for the people from this community and give them an opportunity to express their views and opinions regarding the phenomenon. This study is focussed on this and highlights the influences that gangsterism has on the individual, the family and the community. Furthermore, this study explores a solution to this problem by posing the question, “How can inclusive citizenship education in the classroom address gangsterism in the community?” Therefore, in understanding how a rural community experiences gangsterism, by obtaining insight into various community members’ personal experiences, the researcher was enabled to formulate recommendations as to how inclusive citizenship education could be psycho-educationally used in the classroom to possibly address this scourge in our society. Therefore, this study will be of great value in conceptualising an inclusive educational citizenship element, which can be used in the inclusive classroom. Furthermore, this study will also contribute to our knowledge and help us to gain understanding of gangsterism in smaller communities, which can be used to reduce the crime rates and protect the youth as well as the general community. Through a qualitative, explorative, interpretive descriptive and contextual design, the researcher recruited various professional community members to participate. Data was collected by means of semi-structured, individual interviews, conducted at the local community centre. The interviews were digitally voice-recorded and transcribed for data analysis. After a thematic analysis was performed, a consensus discussion was held with supervisors. Field notes were kept, and observations noted, all of which contributed to rich in-depth data.