Ounongo Repository
The Ounongo Repository (OR) is the institutional repository of Namibia University of Science and Technology. Ounongo means "knowledge. in the Oshiwambo and Otjiherero languages. The OR is administered by the Library, with technical assistance from DICT, and its aim is to collect, organize, manage, store, preserve, publish and make accessible worldwide, the knowledge assets or intellectual output of the University's researchers, staff and post-graduate students. Users may set up RSS feeds to be alerted to new content.
Communities in Ounongo
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Recent Submissions
Empowering African women: An analysis of selected black female authored biographical texts in post-apartheid South Africa
(Namibia University of Science & Technology, 2022-04-22) Hamann, Catherine
This study examined the empowering of African women in selected black female authored biographical texts written in post-apartheid South Africa. Women in South Africa have been disadvantaged largely because of the criminal behaviour of the patriarchal male dominated society. The two texts studied were Khwezi – The Remarkable Story of Fezekile Ntsukela Kuzwayo (Tlhabi, 2017) and No Longer Whispering to Power: The Story of Thuli Madonsela (Gqubule, 2017). The texts were selected because both were written by African female authors and they address contemporary themes that affect the daily livelihoods of women. The texts also represent a true reflection of the difficult challenges encountered by most women in Africa and other parts of the world. The theories of radical feminism as well as trauma and resilience were used in the analysis of the two texts. The study concluded that it is not a waste of resources to educate the girl-child because education is the best method of empowering women. This study has also revealed that a male dominated society can go to any length to disempower women as long as they have the means.
Furthermore, the study concluded that the most dehumanising manner of disempowering a woman through rape. Lastly, the study revealed that biographical writing plays a great role to empower women. The impact of literary texts written by women to represent other women serves as an inspiration because it presents real- life stories. Many women are ashamed to speak out, as a result, they suffer in silence. The stories of both Kuzwayo and Madonsela inspired many women to rise above their circumstances. Hence, women should be encouraged to speak out and report cases of rape or any case of injustices committed against them, despite criticism by society. The study recommends that the patriarchal male society should be educated on the rights of women. In addition, the girl child must be accorded the resources and given the necessary support to acquire an education. It is only when a woman is educated that a society can thrive. It is also imperative to encourage more women to write autobiographies even if someone write it on their behalf. This recommendation is an awareness strategy that can help women to share their various experiences.
Co-Designing And Implementing Independent Journalism And Archiving With The Indigenous San Community In Donkerbos Through A Self-Sustainable Model.
(Namibia University of Science and Technology, 2024-08-15) Kaulbach, Peter
This master thesis explores the impact of a multimedia project in a San community, focusing on podcast episodes and digital storytelling initiatives. The project aimed to amplify the voices and stories of the San people [in Donkerbos] while addressing various topics such as politics, cultural practices, and community projects. Through participatory methodologies, the project engaged community members in the production process, ensuring cultural sensitivity and authenticity. The study responds to the systemic underrepresentation and misrepresentation of San communities in mainstream media, which are often shaped by external perspectives. It investigates the project’s influence on the San community’s awareness, empowerment, cultural preservation, and socio-economic development. It examines the reception of podcast episodes among community members, accounting for their perception and attitude toward the content. Additionally, the thesis explores the effectiveness of digital storytelling initiatives, such as videos on social media platforms, in promoting cultural awareness and challenging stereotypes. Methodologically, the thesis employs a mixed-methods approach, including focus groups, workshops, and content analysis. It draws on theories of indigenous media, participatory communication, and cultural preservation to frame its analysis. The findings show that participants gained technical media skills, expressed increased cultural confidence, and began engaging more actively in the documentation of their community’s stories. Challenges included infrastructural limitations and questions of long-term sustainability. The research contributes to the growing body of literature on indigenous media, community-based research, and cultural preservation. It concludes that participatory multimedia initiatives can enhance cultural resilience and representation, while offering a sustainable framework for community-driven storytelling.
Forecasting The Consumer Price Index in Namibia: A Comparative Analysis of Machine Learning and Statistical Methods
(Namibia University of Science and Technology, 2025-10-02) Elago, Linea.
In emerging countries like Namibia, accurate forecasting of the Consumer Price Index (CPI) is important for evidence-based policy development, effective economic planning, and inflation management. However, despite its importance, there has been limited application of advanced forecasting methods within Namibia’s context. The study addressed this gap by investigating and comparing the performance of traditional statistical methods, AutoRegressive Integrated Moving Average (ARIMA), Holt-Winters exponential smoothing with machine learning methods, Long Short-Term Memory (LSTM) recurrent neural network, and Support Vector Regression (SVR) in forecasting Namibia’s CPI using monthly data from 2013 to 2023. The findings revealed that SVR yielded the lowest Root Mean Square Error (RMSE), indicating higher forecast accuracy compared to other models. The study recommends a shift towards machine learning methods, particularly SVR, for CPI forecasting, given its capability to capture nonlinear trends in economic data. The study further recommends enhancing forecasting methods by incorporating relevant economic indicators such as interest rate, GDP growth, unemployment rate, and government expenditure. The study also emphasises the importance of investing in capacity-building for data science and developing real-time data systems to support policy formulation and CPI monitoring. The research contributes to the growing body of evidence that machine learning can enhance CPI forecasting and decision-making processes in developing countries
Language as an expression of anger in selected Namibian novels: Masked warrior and Complicated
(Journal of Communication and Cultural Trends, 2025-03-07) Kambwale, Elizabeth Ndavavaelao; Woldemariam, Haileleul Zeleke
This article presents a cognitive stylistic study of anger in two Namibian novels: Ndinaelao Moses’ Masked warrior and Malakia Haimbangu’s Complicated. The study evaluated the lexical expressions of anger, figurative expressions, and features of anger discourse. The study aims at probing on how the language in the selected Namibian fictional works deals with anger expressions, particulary on how authors represent societal problems through a cognitive stylistics approach. The study applied textual world theory as a theoretical framework for understanding and analysing the texts. It follows a qualitative approach, with content analysis as the primary data collection method. The results of the study showed that the texts had manipulated and maintained the readers' interest through the use of anger. The study found that words about anger are made more offensive by using figurative language terms. Additionally, the study showed that angry language might be used to show defensiveness, sorrow, or arrogance. The study found that creating writings with anger in them makes readers relate to the characters’ real-world experiences. The findings further established a key communicative function of figurative language that is simplification. Specifically, the study concluded that the strategic deployment of figurative phrases facilitates the effective transmission of complex or abstract concepts, thereby augmenting their accessibility and comprehension for the target audience. The study concluded that discourse influences how angry texts are written. The study suggests the use of alternative language and grammatical expressions that are consistent with Text World Theory, which emphasises the significance of using linguistic and cognitive strategies to create a cohesive and immersive fictional world.
Predictive Modelling of Taxpayer Compliance Behaviour Using Machine Learning At NamRA
(Namibia University of Science and Technology, 2025-07-31) Sifani, Edwin Sifani
This study explored the application of machine learning techniques for predictive modelling of taxpayer compliance behaviour at the Namibia Revenue Agency (NamRA). Multiple classification algorithms were systematically optimised using 5-fold cross-validated Grid Search and Randomised Search, as implemented in the scikit-learn library (v1.2), to enhance predictive accuracy. The hyperparameter search spaces were tailored to each model’s architecture; for instance, Random Forest optimisation included the number of estimators and maximum depth, while Gradient Boosting models emphasised learning rate and structural parameters. The optimisation process yielded notable improvements, with cross-validated accuracy scores ranging from 64% to 68%. The best-performing model, an optimised Random Forest classifier, achieved an accuracy of 68%. These findings demonstrated the efficacy of hyperparameter tuning in improving model performance and underscore the potential of machine learning to support data-driven compliance management at NamRA.
The use of SHAP and LIME in this study provided valuable interpretability of taxpayer compliance predictions, highlighting key factors such as income group, the COVID-19 period, taxpayer registration office, and marital status. These insights align with existing research and reveal how financial capacity, macroeconomic disruptions, and administrative or demographic variables influence compliance. SHAP offers a global view of feature importance, while LIME provides personalised explanations, enhancing trust and communication. Despite modest predictive accuracy, the interpretability benefits support targeted policy interventions and suggest future improvements through richer data and fairness assessments.