Preliminaries into problems to access information – the digital divide and rural communities

Sikhakhane, B.; Lubbe, Sam

doi:10.4102/sajim.v7i3.273

Cited by 5 publications

(5 citation statements)

References 3 publications

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“…Rural women will utilize their low incomes to procure personal essentials. Thus, even if the print media is available gratis, a significant proportion the rural population may still not be able to access the information contained therein (Sikhakhane and Lubbe, 2005 Chi square was used to test the hypothesis that there was no significant relationship between rural women's awareness of improved cookstoves and amount they were willing to pay. The cross-tabulation results are presented in Table 4.…”

Section: Variablesmentioning

confidence: 99%

Improved Cookstoves and User Preferences of Rural Women in Ondo State, Nigeria: Estimates from Random Utility Maximization Model

Ojo¹,

Awolala²,

Ojo³

2019

ijird

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Energy is one of the most important items for human survival apart from air, water and food. It is required for cooking, lighting, heating, boiling and other various domestic needs. Rural population largely depend on the traditional sources of energy in meeting their domestic energy demands while urban population depend essentially on charcoal and fossil fuels. However, high level of poverty and other socioeconomic problems constrains both rural and urban population from access to adequate, reliable and clean energy sources for domestic purposes (Momodu, 2013). Of all domestic fuels in Nigeria, fuelwood remains the commonest partly due to its accessibility, affordability, convenience, tradition and vegetative distribution as other sources namely domestic gas, wind energy, kerosene, solar energy and electricity are either uncommon, modern, costly, sophisticated or required high levels of education and technology to explore, exploit, refine, distribute, store, utilized and maintained (Akut, 2008). Demand for traditional fuel therefore places significant pressure on local forests and woodlands, contributing to deforestation, soil erosion and desertification. Oftentimes, the need for wood is so great that reforestation attempts of badly degraded regions prove impossible because even young trees are rapidly been harvested for cooking fuelwood or charcoal production. In the most severely affected regions, the poorest fuel sources, animal manures, grasses, crop residues, roots and shrubs are also harvested (FAO, 2013). In the developing countries, nearly 3 billion people rely on traditional cooking methods such as an open fire or basic cookstoves, using solid fuels such as wood, charcoal, crop residues and animal dung (GACC, 2011). An energy access assessment across many developing countries reveals that 600 million of this population live in the Sub-Saharan Africa (SSA) where access to modern fuels is as low as 17%, while 69% of the population rely on wood as their primary cooking fuel (UNDP/WHO, 2009). In the efforts by the industrialized economies to mitigate climate change, efforts are being made at reducing Carbon dioxide (CO2) emissions by 50% or more by 2050. Since CO2 is the most significant and prevalent greenhouse gas

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Section: Variablesmentioning

confidence: 99%

Improved Cookstoves and User Preferences of Rural Women in Ondo State, Nigeria: Estimates from Random Utility Maximization Model

Ojo¹,

Awolala²,

Ojo³

2019

ijird

View full text Add to dashboard Cite

show abstract

“…Even if they did not have easier, cheaper internet access or the costs for transport were not low, their parents will have better paying jobs and therefore can afford it compared to these learners from families that are dependent heavily on government grants. According to Sikhakhane and Lubbe (2005) in urban areas, people have easier access to resources that can be used to access information. They further state that the internet has become the main source of information; however, rural populations in Africa, besides not having resources, also do not have the skills to navigate it.…”

Section: Theme 3: Challenges In Accessing the Required Informationmentioning

confidence: 99%

“…Lindokuhle stated: "It stops some learners from applying; if you do not have anyone at home, you can´t have the money". Sikhakhane and Lubbe (2005) argue that in poor, rural families, there is always a need to decide between putting food on the table and investing on educational resources and other costs not relating to the household. Because of the desire to go to university these learners and their families chose to make sacrifices and pay the application fee.…”

Section: Theme 3: Challenges In Accessing the Required Informationmentioning

confidence: 99%

The impact of access to post-secondry education information on "rural origin" students' access to higher education: A case of three schools in Ntabamhlophe, Kwazule-Natal

Njoko

2018

SAJHE

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Inequalities in recruitment, enrolment and throughput rates in higher education among different socioeconomic classes in the democratic South Africa have remained challenging to reduce. Unpacking spatial differences in access to post-secondary education information can strengthen initiatives aimed at reducing such inequalities. Research on access to and success at postsecondary education has based the vulnerability of students from disadvantaged backgrounds heavily on financial constraints. Neglecting the issue of information or Higher Education knowledge which plays a very important role in decision making. It is a challenge for people from disadvantaged backgrounds to find their way through the inexplicable labyrinth of academic forms, financial aid applications, faculty handbooks and prospectuses. Even in Higher Education people have access to formal and informal resources of information. The formal resources which include guidance counsellors, college training in Advanced Placement (AP) courses (in the case of USA), college handbooks and university open days are readily available at wealthier schools than the poorer ones. It is however the informal knowledge networks that really matter in preparing for Higher Education application and admission. This article argues that the lack of exposure to useable post-secondary education information in rural based prospective students results in uninformed and underprepared learners about prospects and possibilities of educational options and opportunities. Hence later on they take longer to complete or even drop out.

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“…The various difficulties and limitations of rural people's access to modern information systems have been demonstrated from the past to the present [1]. The credibility of obtaining assistance from government welfare remains an issue that remains to be addressed.…”

Section: Introductionmentioning

confidence: 99%

Improving the efficiency of machine learning models for predicting blood glucose levels and diabetes risk

Yothapakdee

Charoenkhum²,

Boonnuk

2022

IJEECS

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Fasting <span>blood glucose is used as an indicator in the process of predicting diabetes risk. This research aims to, i) create a model for predicting blood glucose level using data mining algorithms, ii) a selection algorithm was used to select a feature from the correlation of the data, and iii) to compare the model's performance with the classical methods. All clinical data ware recorded and compiled in a database by hospital staff from 2014-2019. In our previous research, the blood glucose prediction model had an acceptable accuracy where 18 patient features were used as input data to the data mining process. In this research, we demonstrated that the random forest classifier and extra tree classifier algorithms have an outstanding in discarding non-critical attributes. And the process of reducing the number of those features has impacted the glycemic prediction model with higher efficiency. Seventeen machine learning algorithms are used to find the best performance models. Our results clearly show that the improved prediction model is more efficient. This experiment has shown that improvements to our proposed model were able to predict blood glucose levels with 99.69% and 99.63% accuracy for random forest classifier, extra tree classifier, and Gaussian process classifier, respectively.</span>

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Preliminaries into problems to access information – the digital divide and rural communities

Cited by 5 publications

References 3 publications

Improved Cookstoves and User Preferences of Rural Women in Ondo State, Nigeria: Estimates from Random Utility Maximization Model

Improved Cookstoves and User Preferences of Rural Women in Ondo State, Nigeria: Estimates from Random Utility Maximization Model

The impact of access to post-secondry education information on "rural origin" students' access to higher education: A case of three schools in Ntabamhlophe, Kwazule-Natal

Improving the efficiency of machine learning models for predicting blood glucose levels and diabetes risk

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