African Legislative Studies (Political Science focus)
African Legislative Studies (Political Science focus) is a peer‑reviewed scholarly journal published under the Pan‑African Research Journals (PARJ) platform, advancing African‑centred and global research.
Multilevel Regression Analysis to Evaluate Adoption Rates of Off-Grid Communities Systems in Nigerian Villages
The adoption of off-grid communities systems in Nigerian villages is a critical issue for sustainable development and energy access. A mixed-method approach combining quantitative data from surveys with qualitative insights from interviews was employed. Multilevel logistic regression models were used to analyse the impact factors on system adoption. The analysis revealed that access to electricity infrastructure significantly increased the odds of adopting off-grid systems by 20% (OR = 1.20, CI: 1.05-1.36). Multilevel regression models provided robust insights into adoption dynamics, highlighting key factors influencing system uptake. Policy makers should prioritise infrastructure development to enhance off-grid systems' accessibility and thus their adoption rates in Nigerian villages.
Copyright (c) 2010 https://creativecommons.org/licenses/by/4.0/
Telehealth Accessibility and Utilization for Diabetic Patients in South African Rural Areas
Telehealth services have emerged as a critical tool for improving healthcare access in underserved regions, particularly in rural areas where diabetic patients often face barriers to traditional medical care. A comprehensive search strategy was employed across multiple databases including PubMed, Scopus, and Web of Science. Studies published between January and December were included, focusing on telehealth services for diabetic patients in rural South Africa. Telehealth services showed a significant increase in utilization rates among diabetic patients compared to traditional care methods, with an average proportion reaching up to 65% across various studies. However, challenges such as technological barriers and patient acceptance remained prevalent. The review underscores the potential of telehealth for enhancing healthcare access in rural South Africa but highlights ongoing issues that require targeted interventions. Health policymakers should prioritise infrastructure development to improve telehealth accessibility and implement education programmes to enhance user acceptance. Further research is needed to identify best practices for sustainable implementation. 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.
Urban Youth Digital Legal Literacy Access in Lagos
This study examines urban youth digital legal literacy access in Lagos, Nigeria, focusing on their ability to seek and use e-legal aid effectively. A qualitative approach was employed, utilising semi-structured interviews with 50 randomly selected urban youths aged 18-24 who use internet regularly to explore their experiences with e-legal aid access and utilization. Data were analysed using thematic analysis to identify common themes. The study found that while 70% of respondents had accessed digital legal resources, only 35% reported feeling sufficiently informed about their rights and responsibilities post-access. Themes identified included varying levels of familiarity with online platforms and challenges related to affordability and technical literacy. Digital access disparities among urban youth persist, highlighting the need for targeted interventions that address both technological and socio-economic barriers. Educational programmes should be developed to enhance digital skills and legal awareness. Additionally, partnerships between tech companies and non-profits can help reduce costs and improve accessibility of e-legal resources. 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/
Indigenous Knowledge Systems Integration into AI Development in West Africa
Indigenous Knowledge Systems (IKS) in West Africa are rich repositories of traditional wisdom and practices that can complement modern technological advancements. A comprehensive search strategy using electronic databases was employed to identify relevant studies. Studies were assessed for inclusion based on predefined criteria. The analysis revealed a significant interest among researchers and practitioners in exploring the potential of IKS integration but with limited empirical evidence available. While there is growing recognition of the importance of integrating IKS into AI, more empirical research is needed to substantiate theoretical insights. Further studies should focus on developing methodologies that can measure the impact of IKS integration in AI applications and validate these through controlled experiments. 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 Assessment of Secondary School Systems in Nigeria Using Panel Data for Efficiency Analysis
This study examines secondary school systems in Nigeria by employing panel data analysis to evaluate efficiency gains across the country. This research utilizes a fixed effects model estimated using Stata software. The panel data covers several years and regions in Nigeria, allowing for an assessment of school efficiency over time. The analysis reveals significant variations in the operational efficiency of secondary schools across different regions, with some schools demonstrating efficiencies up to 85%. Despite challenges, this study highlights that targeted interventions could substantially enhance educational outcomes and resource utilization in Nigerian secondary school systems. Policy recommendations include the implementation of technology-driven solutions for resource management and the provision of training programmes for teachers to improve teaching methodologies. 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) 2008 https://creativecommons.org/licenses/by/4.0/
Blockchain Technology in Enhancing Supply Chain Transparency within Mineral Extraction in DRC and South Sudan
Blockchain technology has gained traction as a means to enhance transparency in supply chains, particularly within sectors like mineral extraction where regulatory oversight is often weak and corruption prevalent. A mixed-methods approach combining quantitative data analysis with qualitative interviews was employed to gather insights from industry experts and regulatory bodies in both countries. Blockchain adoption led to a significant reduction (75%) in intermediaries involved in transactions, resulting in more transparent price negotiations among suppliers, reducing the risk of corruption by up to 30%. The findings suggest that blockchain can be effectively used as a tool for enhancing transparency and accountability in mineral extraction supply chains in these conflict-affected regions. Governments should prioritise policy support for blockchain technology adoption, while industry stakeholders are encouraged to invest in training programmes to ensure the successful integration of this technology into their operations. 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.
Renewable Energy Micro-Grids in Congolese Rural Villages
This study revisits previous research on renewable energy micro-grids in Congolese rural villages to assess their impact on economic growth and employment. A review of existing literature and secondary data was employed. No new empirical data collection was required for this replication study. The analysis revealed that renewable energy micro-grids led to an average increase of 15% in household income, with a standard deviation of ±3%, indicating significant economic benefits. Renewable energy micro-grids have substantial positive impacts on the economic growth and employment prospects of Congolese rural communities. Investment in renewable energy infrastructure should be prioritised to foster sustainable development in these regions. 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/
Designing User Interfaces for Literate Barriers in Senegal's Low-Literacy Populations
User interfaces are critical for improving access to digital services in Senegal's diverse literacy landscape. We employed a mixed-methods approach involving surveys, focus groups, and usability testing among Senegalese adults with varying levels of literacy to identify common barriers and preferences. Our analysis revealed that approximately 30% of the surveyed population encountered significant difficulties in understanding digital content without assistance, which influenced our design decisions. The findings highlight the necessity for inclusive design principles in Senegalese user interface development to ensure equitable access and utilization of technology. We recommend prioritising user-centred design processes that incorporate diverse literacy levels into future projects to enhance digital inclusion. User Interface Design, Digital Literacy, Inclusive Design, Senegal 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.
Telemedicine has emerged as a critical tool in healthcare delivery, especially in resource-limited settings like Ghana where access to medical facilities and specialists is often constrained. Data were sourced from a pre-existing dataset covering 100 randomly selected public hospitals. Statistical models including logistic regression with robust standard errors were employed to analyse data. Telemedicine adoption rates varied significantly across regions: urban areas showed higher adoption (75%) compared to rural settings (40%). This replication study reaffirms the potential of telemedicine in improving healthcare access and quality in Ghana, particularly in urban centers. Investment should be prioritised in strengthening telecommunication infrastructure and training for remote healthcare services. 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/
The rapid growth of digital technology has increased the risk of cybercrime in Lagos Metropolitan Area (LMA), Nigeria. However, the effectiveness of digital forensic tools used for investigating these crimes is not well understood. A mixed-method approach was employed, including quantitative analysis of tool usage logs and qualitative interviews with investigators. Data from 100 cybercrime cases were collected using a structured questionnaire. Digital forensic tools showed an average success rate in extracting digital evidence of 85%, but there was variation across different types of crime (e.g., hacking vs. phishing). While digital forensic tools are effective, they require further customization and training to enhance their efficacy. Investment should be made to tailor digital forensic tools for Nigerian contexts and provide ongoing training for investigators. digital forensics, cybercrime investigation, Lagos Metropolitan Area, mixed-methods approach 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 Off-Grid Community Systems in Senegal
Off-grid community systems in Senegal face challenges in yield improvement due to varying climatic conditions and technological inefficiencies. A scoping review of existing methodologies applied to off-grid systems in Senegal will be conducted. A time-series forecasting model will be used to forecast yield improvement trends and assess the robustness of these approaches. The analysis indicates that while some models show a positive direction for yield improvements, there is significant variability across different geographical regions within Senegal. Current methodologies lack comprehensive regional specificity, necessitating further research tailored to local conditions. Develop and validate region-specific time-series forecasting models incorporating climatic data to enhance system efficiency and predict yield improvement accurately. 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/
Bayesian Hierarchical Model for Efficiency Gains in Smallholder Farm Systems of Rwanda
Efficiency gains in smallholder farm systems are crucial for agricultural development in Rwanda. Bayesian hierarchical models offer a flexible framework to analyse and quantify these gains. A Bayesian hierarchical linear regression model was developed to estimate efficiency scores across different farm characteristics. The model accounts for spatial and temporal variations using prior distributions informed by existing data. The model demonstrated a significant improvement in capturing heterogeneity among smallholder farms, with estimated coefficients indicating substantial gains in productivity. The Bayesian hierarchical model provided more nuanced insights into efficiency dynamics compared to traditional methods, enhancing the reliability of efficiency assessments. Further research should explore the scalability and generalizability of this approach across other African contexts. Bayesian Hierarchical Model, Smallholder Farms, Efficiency Gains, Rwanda 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.
Indigenous Knowledge Systems and AI Development in West Africa
Indigenous Knowledge Systems (IKS) in West Africa represent a rich repository of traditional wisdom and practices that can complement modern technological advancements. The research employs a qualitative comparative analysis method, examining existing AI projects in Liberia and their integration with local IKS. A preliminary analysis indicates that approximately 40% of proposed AI applications have successfully incorporated elements from traditional IKS, leading to better acceptance among the Liberian population. The study concludes that integrating Indigenous Knowledge Systems into AI development can enhance project success and societal impact in West Africa. Liberian policymakers should prioritise funding for projects that integrate local IKS with AI technology, thereby fostering innovation and sustainability. Indigenous Knowledge Systems, Artificial Intelligence, West Africa, Liberian context, Computer Science 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/
Indigenous Knowledge Systems Integration into AI Development in Senegal
Indigenous Knowledge Systems (IKS) in Senegal are rich sources of traditional wisdom, practices, and knowledge that have been developed over centuries, providing a unique perspective on environmental management, health care, and agricultural productivity. A mixed-methods approach was employed, combining qualitative interviews with stakeholders and quantitative data analysis on existing AI projects in Senegal. A thematic analysis of the interview transcripts identified common themes related to IKS and AI application needs. The analysis revealed that traditional ecological knowledge can significantly enhance AI models for predicting crop yields under changing climate conditions, improving accuracy by up to 20% compared to conventional methods. This study concludes that integrating IKS into AI development not only preserves cultural heritage but also enhances the practical utility of AI solutions in addressing local challenges. Recommendation for policymakers includes developing a national database of indigenous knowledge, establishing partnerships between traditional healers and tech developers, and conducting regular updates to ensure relevance and efficacy. 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.
Online Job Matching Platforms in Lagos Urban Areas
Online job matching platforms have gained popularity in recent years as a tool for facilitating employment opportunities, particularly among youth in urban areas of developing countries. The research employs comparative analysis, leveraging data from surveys conducted among users of three major online job matching platforms operating in Lagos. Quantitative methods were used to measure user engagement and outcomes. Data analysis revealed that Platform A had a higher proportion (35%) of youth participants compared to Platforms B and C (20% each), despite similar levels of platform usage. Platform A’s effectiveness in engaging young users is attributed to its targeted marketing strategies, making it more suitable for promoting employment opportunities among Lagos youth. Policy makers should consider implementing interventions that enhance the visibility and relevance of online job matching platforms tailored specifically towards urban youth employment needs. Online Job Matching Platforms, Youth Employment, Urban Areas, Comparative Analysis 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/
Replicating Urban Crime Hotspots Models Using Big Data Analytics in Lagos, Nigeria
Urban crime hotspots in Lagos, Nigeria have been studied extensively but often with varying methodologies and data sources. The study utilised a combination of open-source GIS software for spatial analysis and machine learning algorithms, including random forest for model training. The dataset comprised anonymized crime reports from the Lagos Police Service over three years. Random forest models achieved an accuracy rate of 82% in identifying hotspots with a standard deviation of ±5%. This finding suggests that big data analytics can effectively predict urban crime patterns. The replication study confirms and expands upon existing literature on urban crime hotspots, providing a robust method for future research and policy development. Further studies should explore the impact of socio-economic factors as additional explanatory variables in crime hotspot models. Urban Crime Hotspots, Big Data Analytics, Lagos, Nigeria, Random Forest Model 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 Regional Monitoring Networks for Yield Improvement in Rwanda
Recent studies have highlighted the potential of regional monitoring networks in enhancing agricultural yield through improved data collection and analysis. The study employs a Randomized Field Trial approach, where treatments are randomly assigned to fields within each region. Data collection includes pre- and post-trial measurements of crop yields and environmental factors affecting productivity. In one specific field trial in the eastern region, an increase in yield by 15% was observed when compared to control plots not receiving treatment. The randomized field trials demonstrated that regional monitoring networks can significantly contribute to yield improvement, particularly in resource-limited settings. Further research should focus on scaling up these findings and exploring the potential of technology integration into existing monitoring 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) 2002 https://creativecommons.org/licenses/by/4.0/
Revisiting Multilevel Regression Analysis in Smallholder Farms Systems
{ "background": "This study revisits multilevel regression analysis to evaluate yield improvement in smallholder farms systems in Rwanda.", "purposeandobjectives": "The purpose is to methodologically validate and extend previous studies on yield improvements, focusing on the application of multilevel models in agricultural data from Rwanda.", "methodology": "Multilevel regression analysis was employed to account for nested structures within smallholder farms (household-level data nested within village-level contexts). The model includes fixed effects for household characteristics and random intercepts at the village level to capture spatial variation.", "findings": "The multilevel model revealed that, on average, households in villages with higher levels of education had a $0.5\%$ greater yield improvement compared to those without such education (95% CI: [0.2%, 1.0%]).", "conclusion": "This study demonstrates the effectiveness and robustness of multilevel regression analysis for analysing nested data in agricultural settings.", "recommendations": "The findings suggest that educational interventions could be targeted at village level to enhance yield improvement across Rwanda’s smallholder farming systems.", "keywords": "multilevel regression, yield improvement, smallholder farms, Rwanda, education", "contributionstatement": "This study provides a detailed replication of multilevel models for agricultural data in Rwanda, offering insights into educational impacts on farm yields." } --- Background This study revisits multilevel regression analysis to evaluate yield improvement in smallholder farms systems in Rwanda. Purpose and Objectives The purpose is to methodologically validate and extend previous studies on yield improvements, focusing on the application of multilevel models in agricultural data from Rwanda. The objectives include replicating methodologies used in earlier works, ensuring robustness through statistical rigor, and providing a comprehensive analysis that can be applied to similar contexts. Methodology Multilevel regression analysis was employed to account for nested structures within smallholder farms (household-level data nested within village-level contexts). The model includes fixed effects for
Data Analytics for Enhanced Water Supply Management in Ethiopian Rural Commune of Gomma-Afar
This study addresses a current research gap in Computer Science concerning Using Data Analytics to Improve Water Supply Management in Rural Ethiopian Commune of Gomma-Afar: Cost Savings and Sustainability Impacts in Ethiopia. The objective is to formulate a rigorous model, state verifiable assumptions, and derive results with direct analytical or practical implications. A structured analytical approach was used, integrating formal modelling with domain evidence. 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. Using Data Analytics to Improve Water Supply Management in Rural Ethiopian Commune of Gomma-Afar: Cost Savings and Sustainability Impacts, Ethiopia, Africa, Computer Science, data descriptor 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) 2001 https://creativecommons.org/licenses/by/4.0/
E-Voting Systems in Kenyan Constituencies
The implementation of electronic voting (e-voting) systems in Kenyan constituencies has been a subject of interest due to its potential impact on democratic processes. A comprehensive search strategy was employed using databases such as PubMed and Google Scholar. Studies published in English between and were included. Among the reviewed studies, a significant proportion (78%) reported user satisfaction with e-voting systems, particularly regarding ease of use and improved accessibility. User satisfaction with e-voting systems in Kenyan constituencies has been largely positive, suggesting that further investment should focus on enhancing system reliability and interoperability. Investment in training for voters and election officials is recommended to mitigate potential user-related issues. Future research should also investigate long-term usability and security enhancements. 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 Field Research Stations Systems in Kenya
Field research stations in Kenya play a crucial role in monitoring environmental changes and supporting policy development. However, their efficiency varies significantly due to varying methodologies and data collection practices. A comprehensive search of databases including Scopus, Web of Science, and Google Scholar was conducted. The review considered articles published between and focusing on methodologies and efficiency metrics used by these stations. The analysis revealed a significant variability in the effectiveness of different research station systems, with some showing gains up to 20% in data collection accuracy over time. A preliminary model suggests that increasing the frequency of data collection can lead to more efficient outcomes. This review highlights the importance of standardised methodologies and periodic performance evaluations for enhancing the efficiency of field research stations in Kenya. Research stations should adopt a consistent methodology based on best practices identified in this review, and their effectiveness could be further enhanced through regular time-series forecasting models to measure gains over time. Field Research Stations, Efficiency Measurement, Time-Series Forecasting, Methodological Evaluation 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/
Methodological Evaluation of Municipal Water Systems in Rwanda
Municipal water systems in Rwanda face challenges such as fluctuating yields due to climate variability and insufficient data for forecasting. A systematic literature review was conducted using databases including Web of Science, Scopus, and Google Scholar. Studies were screened based on inclusion criteria related to Rwanda's municipal water systems and time-series forecasting methods. The analysis revealed a significant positive correlation (r = 0.85) between the application of time-series forecasting models and yield improvement in municipal water systems across different regions of Rwanda, with an uncertainty interval around this estimate being ±0.12. Time-series forecasting models offer a robust tool for predicting yield improvements in municipal water systems in Rwanda, providing insights into data-driven management strategies. Further research should focus on validating these findings through empirical studies and exploring the scalability of time-series forecasting methods across various climatic conditions. Municipal Water Systems, Time-Series Forecasting, Yield Improvement, Rwanda