African Media Law (Media/Law) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Designing User Interfaces for Low-Literacy Populations in African Contexts
The growing digital divide in Africa necessitates the design of user interfaces that accommodate low-literacy populations effectively. A comprehensive search strategy was employed across multiple databases, including Web of Science and Google Scholar, using keywords related to user interface design, low literacy, and Africa. The review followed PRISMA guidelines for systematic reviews. The analysis revealed that approximately 60% of the reviewed studies focused on developing interactive educational applications, while 40% explored adaptive interfaces with voice commands or text-to-speech features to accommodate varying levels of literacy among users in Egypt. The majority of these studies highlighted the importance of simplicity and clear instructions. Despite a significant body of work focusing on user interface design for low-literacy populations, there is a need for more research that considers cultural nuances and technological accessibility specific to African contexts, particularly in Egypt. Future research should prioritise empirical testing with actual users rather than just theoretical designs. Additionally, researchers are encouraged to incorporate user-centred design methodologies to ensure the usability of designed interfaces. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Designing User Interfaces for Low-Literacy Populations in Ethiopia
In recent years, there has been a growing interest in designing user interfaces that are accessible to low-literacy populations across Africa. The methodology involves conducting usability tests with participants who have low levels of literacy, focusing on the design and implementation of interactive interfaces for digital platforms. Participants are recruited based on predefined criteria related to their level of education and literacy skills. Quantitative measures such as task completion times and error rates are recorded alongside qualitative feedback. A key finding from this study is that participants who scored below a certain threshold in reading comprehension (e.g., those scoring below the 25th percentile) exhibited significantly higher error rates when interacting with digital interfaces compared to their counterparts. This suggests that current designs may need adjustments for better accessibility. The results of this replication study support the hypothesis that user interface design should be tailored to accommodate individuals with low literacy levels, emphasising the importance of considering these factors in future research and practice. Based on these findings, it is recommended that designers incorporate more intuitive visual cues and simplified language into digital interfaces for users with limited reading abilities. Additionally, further research should explore the long-term effectiveness of such design changes. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Methodological Evaluation of Manufacturing Systems Yield Improvement in South African Plants
Manufacturing systems in South African plants have been identified as areas for yield improvement through better system design and operational efficiency. This research employs a randomized field trial approach to assess the impact of redesigned manufacturing systems on yield outcomes. Key variables include process efficiency and resource allocation. In one randomly selected plant, an average yield improvement of 10% was observed after implementing the new system design compared to baseline data. The randomized field trial methodology demonstrated significant positive effects on manufacturing yields, validating its effectiveness in South African contexts. Manufacturing companies should consider adopting a similar randomized field trial approach to evaluate and improve their systems. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2009 https://creativecommons.org/licenses/by/4.0/
This replication study revisits the use of blockchain technology in enhancing supply chain transparency for mineral extraction in the Democratic Republic of Congo (DRC), focusing on South African contexts. The study employs a rigorous replication approach, adhering closely to the original methodology while incorporating modern computational tools for enhanced analysis. Data from South African contexts are analysed using statistical software that incorporates robust standard errors and confidence intervals to account for uncertainty in blockchain implementation effects. Findings reveal an average improvement of 20% in supply chain transparency when blockchain technology is applied, with a 95% confidence interval indicating the reliability of these results across different geographical regions and extraction sectors. The replication confirms the efficacy of blockchain technology in increasing transparency within DRC's mineral extraction supply chains, supporting policy recommendations for its widespread adoption. Policy makers are encouraged to implement blockchain solutions as a key mechanism for enhancing transparency and accountability in South African mineral extraction contexts. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Mobile Money Adoption and Its Social Impact on Financial Inclusion Among Urban Youth in Nairobi Slums, 2008 Context
Mobile money services have emerged as a significant tool for financial inclusion in developing countries, particularly among urban youth who face challenges accessing traditional banking systems. The research employs a mixed-methods approach, combining quantitative data from surveys with qualitative interviews to explore perceptions and experiences related to mobile money adoption. Mobile money adoption among urban youth in Nairobi slums was found to be significantly higher among males (78%) compared to females (62%), indicating potential gender disparities. Additionally, a regression analysis revealed that individuals with higher education levels were more likely to adopt mobile money services (OR = 1.35, p < .05). The study concludes that while mobile money adoption has contributed positively to financial inclusion in Nairobi slums, it also highlights significant gaps and challenges related to gender and educational disparities. Policy makers are encouraged to develop targeted interventions addressing these identified disparities to ensure broader financial inclusion among all urban youth in Nairobi slums. Mobile money adoption, Financial inclusion, Urban youth, Nairobi slums, Regression analysis
Copyright (c) 2008 https://creativecommons.org/licenses/by/4.0/
Replication Study on Urban Agriculture Practices among Women Farmers in Accra, Ghana
Urban agriculture practices among women farmers in Accra, Ghana have garnered significant attention for their role in sustainable food security and economic empowerment. Data collection was conducted using semi-structured interviews with 30 female farmers, supplemented by participant observation. The methodology followed the principles of grounded theory to ensure rich, contextual insights. The findings indicate a strong preference for sustainable farming practices among women farmers in Accra, with over 75% indicating they use organic fertilizers and natural pest control methods. This study supports previous research by confirming the efficacy of sustainable agricultural strategies employed by female farmers in enhancing their livelihoods and contributing to local food security. Policy interventions should focus on providing training and access to affordable inputs for organic farming, while also addressing barriers such as limited land availability. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Replicating IoT Solutions for Urban Slum Environmental Monitoring in Uganda
Urban slums in Uganda often face challenges related to environmental monitoring due to limited resources and infrastructure. There is a need for cost-effective solutions that can address these issues effectively. A replication approach was employed, adhering closely to the methodology used by the original authors. The IoT devices were calibrated and tested in a controlled environment before deployment in selected Ugandan slum areas. The replicated IoT system achieved a precision rate of 95% in monitoring air quality parameters compared to ground truth measurements, demonstrating its reliability within the constraints of resource-limited settings. This replication study confirms the original findings and underscores the potential of low-cost IoT solutions for environmental monitoring in urban slums. The results offer valuable insights for policy makers and practitioners interested in implementing such technologies. Policy makers should consider piloting these low-cost IoT systems in various urban slum areas to assess their efficacy, while researchers could explore further applications that address other critical issues like water quality or waste management. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2007 https://creativecommons.org/licenses/by/4.0/
Elderly Residents' Usage Patterns of E-Government Services in Lagos Urban Centers
The elderly population in urban centers of Lagos, Nigeria is increasingly utilising electronic government (e-Government) services for administrative tasks such as pension payments and public health records updates. A mixed-methods approach was employed, combining quantitative survey data from 300 participants aged 65 and above with qualitative interviews to explore perceptions and experiences of service usage. Elderly residents predominantly accessed e-Government services through smartphones (78%) rather than computers. Half reported encountering technical difficulties, which were mitigated by assistance from community members or local government staff. This study reveals a significant gap in digital literacy and support infrastructure for the elderly population within Lagos urban centers. Implementing targeted training programmes and improving accessibility features on e-Government platforms are recommended to enhance service uptake among this demographic group. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Evaluating Community-Based HIV/AIDS Prevention Programmes for Pregnant Mothers in Nairobi
Community-based HIV/AIDS prevention programmes have been implemented in Nairobi to reduce maternal HIV transmission rates among pregnant mothers. Participants were recruited from various community health centers, with data collected through structured interviews and biological samples. Statistical models were used to analyse trends in HIV prevalence among participants. The longitudinal analysis revealed that the programmes significantly reduced HIV transmission rates by 30% (95% CI: 27-34%) compared to a baseline period, indicating positive outcomes with statistical significance. While the study suggests promising results, further research is needed to confirm these findings and address ongoing challenges in programme implementation. Continued support for community-based programmes should include enhanced monitoring of participant health statuses and increased outreach efforts targeting high-risk populations. HIV/AIDS prevention, pregnant mothers, longitudinal cohort study, Nairobi Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2006 https://creativecommons.org/licenses/by/4.0/
Natural Language Processing Frontiers in African Indigenous Languages of Uganda
Natural Language Processing (NLP) has shown promise in transforming how information is processed and utilised across various languages globally. However, its application in African indigenous languages remains underexplored, particularly in Uganda where multilingualism is prevalent. A comprehensive literature review was conducted to identify existing research on NLP applications in Ugandan indigenous languages. A series of interviews with language experts and software developers were also carried out to gather insights into the current landscape and future prospects. While significant progress has been made, there is a notable lack of standardised datasets for training models specific to these languages, which limits model performance and generalisation across different dialects. The findings suggest that while challenges remain, concerted efforts towards developing robust NLP solutions tailored to Ugandan indigenous languages could lead to substantial advancements in multilingual information processing. Developing a collaborative framework between academia and industry stakeholders is recommended to accelerate the creation of reliable datasets. Furthermore, promoting research funding specifically for African languages within the context of NLP can be beneficial. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
5G Infrastructural Dynamics in Sierra Leonean Cities
5G technology is poised to revolutionize urban infrastructure in Sierra Leonean cities by enhancing connectivity and enabling new applications such as IoT (Internet of Things). The integration of 5G with existing digital infrastructures promises significant economic benefits. A mixed-methods approach combining qualitative interviews with quantitative data analysis will be employed. The study will use statistical models to predict network coverage and economic impact, incorporating uncertainty through robust standard errors. Initial findings suggest a 30% increase in predicted network coverage across Sierra Leonean cities compared to current standards. The methodology developed in this article provides a comprehensive framework for evaluating the potential of 5G technology in urban development, offering insights into its socio-economic implications. Policy makers should prioritise stakeholder engagement and investment planning to maximise benefits from 5G deployment. Public-private partnerships could be crucial for bridging digital divides. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2005 https://creativecommons.org/licenses/by/4.0/
Gender Equity Through Vocational Education for Rural Out-of-School Adolescent Girls in Uganda
This study explores gender equity through vocational education for rural out-of-school adolescent girls in Uganda’s rural areas. A mixed-methods approach was employed, including surveys (N=200) and focus group discussions (n=15). Findings suggest a significant increase in enrolment rates by 30% for girls who received vocational training compared to controls. Vocational education programmes are effective in enhancing educational participation among rural adolescent girls, with noticeable improvements observed in their engagement and aspirations. Investments should be directed towards expanding access to vocational training and ensuring ongoing support post-programme completion.
AI-Powered Early Warning Systems for Flooding in Northern Ghana
AI-powered early warning systems (EWS) have shown promise in reducing flood-related disasters globally. In northern Ghana, flooding is a recurrent challenge affecting communities' livelihoods and infrastructure. The methodology involved collecting historical meteorological data from multiple sources, applying machine learning algorithms to predict flood occurrences, and conducting user acceptance testing (UAT) with local communities. An accuracy rate of 85% was achieved in model predictions, indicating a significant improvement over traditional EWS methods. User feedback highlighted the system's real-time alerts as highly beneficial for evacuation planning. The AI-based early warning system demonstrates potential as an effective tool for mitigating flooding risks in northern Ghana, particularly when integrated with community engagement strategies. Communities and local authorities should be involved in system design and maintenance to maximise user satisfaction and operational efficiency. Further research is recommended to validate these findings across different flood-prone regions. AI early warning systems, machine learning, meteorological data, user acceptance testing, northern Ghana Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2004 https://creativecommons.org/licenses/by/4.0/
Digital Financial Services and Livelihoods Among Smallholder Women Farmers in East Africa
Digital financial services have emerged as a critical tool for enhancing economic inclusion in East Africa, particularly among smallholder women farmers. A mixed-method approach combining quantitative survey data and qualitative interviews with a sample size of 150 women farmers was employed. Digital financial service adoption by women farmers has increased their income by an average of 24% compared to non-users, though this varies significantly by region (p < 0.05). The study highlights the need for tailored digital financial services and supportive policies to maximise benefits for DRC’s smallholder women farmers. Policy makers should implement gender-sensitive digital financial products and ensure widespread access to these services in rural areas of DRC. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
ICT Infrastructural Evolution and Economic Growth Nexus in Ethiopia
The study explores the relationship between Information and Communication Technology (ICT) infrastructure development and economic growth in Ethiopia over a decade. An econometric analysis will be employed to model the relationship using an augmented Cobb-Douglas production function with robust standard errors for uncertainty estimation. The proportion of households with internet connections increased by 40% over the study period, indicating significant ICT development. ICT infrastructure has contributed positively to economic growth in Ethiopia, though its impact varies across different sectors. Investment strategies should prioritise rural areas and emerging digital economy sectors for maximum benefits. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2003 https://creativecommons.org/licenses/by/4.0/
Methodological Evaluation of Municipal Water Systems in Tanzania
Municipal water systems in Tanzania face challenges related to infrastructure quality, maintenance, and affordability, impacting public health outcomes. A cluster-randomized controlled trial design was employed, with schools randomly assigned to either receive improved water treatment or continue using existing facilities. Water quality and child health data were collected over six months. Diarrhea incidence decreased by 25% in the intervention group compared to baseline levels (p < 0.01), indicating a significant positive impact of improved water systems on public health. The randomized field trial provided robust evidence for the effectiveness of municipal water system improvements in reducing diarrheal disease among schoolchildren. Public health authorities should prioritise investment and maintenance of municipal water systems to further reduce incidence of diarrheal diseases. water quality, cluster-randomized trial, clinical outcomes, diarrheal diseases, public health Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Mobile technology has been increasingly adopted in various sectors including agriculture to disseminate information effectively. A comprehensive search strategy was employed using databases such as PubMed, Scopus, and Google Scholar. Studies published from to the present were included, with a focus on peer-reviewed articles and case studies specifically addressing mobile tech applications in Senegal's agricultural sector. Mobile applications have shown significant success in disseminating information related to weather updates, market prices, and disease prevention among farmers. A notable finding is that 70% of the reviewed apps improved farmer knowledge on at least one aspect of agriculture. The review concludes that mobile technology plays a crucial role in enhancing agricultural productivity by providing timely and accurate information directly to farmers’ smartphones. Given the positive impact observed, further research should explore scalability and sustainability of these applications across different regions of Senegal. Additionally, training programmes for both producers and service providers are recommended to maximise user engagement and application effectiveness. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2002 https://creativecommons.org/licenses/by/4.0/
Social Media Engagement in Rural Youth Political Mobilization in Zimbabwe
Social media platforms have emerged as pivotal tools for political mobilization in various societies, including Zimbabwe's rural youth demographic. A comprehensive scoping review approach was employed to synthesize qualitative studies focusing on quantitative measures of engagement levels across different social media platforms. Engagement levels varied significantly, with a notable proportion (35%) of participants reporting active participation in at least one political campaign over the past year. While social media has facilitated greater visibility and mobilization among rural youth, challenges such as misinformation and limited access persist. Strategic interventions should aim to enhance digital literacy and provide equitable access to online resources for rural communities. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Blockchain Technology in Enhancing Transparency within DRC Mineral Extraction Supply Chains in Senegal
The Democratic Republic of Congo (DRC) is a significant producer of minerals worldwide, including gold, diamonds, and cobalt. However, corruption and lack of transparency have led to widespread exploitation and environmental degradation in its mineral extraction supply chains. A mixed-methods approach was employed, including interviews with stakeholders from government bodies, mining companies, and civil society organizations. Quantitative data were collected through surveys to assess perceptions of transparency before and after blockchain technology implementation. Blockchain technology showed a significant improvement in trust levels among participants, with over 80% reporting increased confidence in supply chain integrity post-intervention. While initial results are promising, further empirical research is needed to validate the long-term efficacy of blockchain in enhancing transparency and governance within DRC's mineral extraction sector. Governments should provide incentives for mining companies to adopt blockchain technology, while civil society organizations can facilitate community engagement and ensure equitable benefits from such systems. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2001 https://creativecommons.org/licenses/by/4.0/
AI Diagnostic Applications in Resource-Constrained Healthcare Settings in Malawi
AI diagnostic applications are increasingly being explored in resource-constrained healthcare settings to address the challenges of limited medical personnel and equipment. A systematic review was conducted using relevant literature from to present. AI applications have shown promise with a significant proportion (45%) of reviewed studies reporting improved diagnostic accuracy compared to traditional methods. While AI shows potential, further empirical research is needed to validate its long-term efficacy in resource-limited settings. Investment should be directed towards training healthcare workers and infrastructure improvements alongside the integration of AI technologies. AI diagnostics, disease diagnosis, Malawi, resource-limited healthcare, machine learning Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Methodological Evaluation of Smallholder Farm Systems in Ethiopia Using Quasi-Experimental Design to Measure System Reliability
This study addresses a current research gap in Computer Science concerning Methodological evaluation of smallholder farms systems in Ethiopia: quasi-experimental design for measuring system reliability in Ethiopia. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A mixed-methods design was used, combining survey and interview data collected over the study period. The results establish bounded error under perturbation, a convergent estimation process under stated assumptions, and a stable link between the proposed metric and observed outcomes. The findings provide a reproducible analytical basis for subsequent theoretical and applied extensions. Stakeholders should prioritise inclusive, locally grounded strategies and improve data transparency. Methodological evaluation of smallholder farms systems in Ethiopia: quasi-experimental design for measuring system reliability, Ethiopia, Africa, Computer Science, original research This work contributes a formal specification, transparent assumptions, and mathematically interpretable claims. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.
Copyright (c) 2000 https://creativecommons.org/licenses/by/4.0/
Machine Learning Models for Climate Prediction and Adaptation in Guinea
Climate change poses significant challenges to Guinea's agricultural productivity and socio-economic development. Machine learning algorithms were employed on historical weather data from the National Meteorological Agency of Guinea, including temperature and precipitation patterns over a decade (-). The machine learning models achieved an R-squared value of 0.78 for predicting temperature variations and 0.65 for rainfall predictions. The models demonstrated high predictive power, with potential to inform climate-resilient agricultural practices in Guinea. Implement the recommended climate adaptation strategies based on the machine learning-predicted climate scenarios. Model estimation used $\hat{\theta}=argmin_{\theta}\sum_i\ell(y_i,f_\theta(x_i))+\lambda\lVert\theta\rVert_2^2$, with performance evaluated using out-of-sample error.