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Record W1488371720 · doi:10.15353/joci.v10i2.2657

An African Rural Internet Network, and its interactions with Academics

2013· article· en· W1488371720 on OpenAlexvenueno aff
Gertjan van Stam, Darelle van Greunen

Bibliographic record

VenueThe Journal of Community Informatics · 2013
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyLast mile (transportation)BusinessThe InternetRural areaInternet accessService (business)Economic growthDigital divideScarcityService providerUniversal serviceDeveloping countryMarketingTelecommunicationsEconomicsPolitical scienceGeographyEngineeringMileComputer science

Abstract

fetched live from OpenAlex

Creating ICT access in the rural areas of Africa is a complex challenge. In these areas, telecommunications infrastructure is provided at a very high cost that may not be justified by the resulting use and effects of the telecommunications network. Affordable access to ICTs in rural areas can be frustrated at the supply as well as the demand end of the service-provision chain. To supply ICTs and related services in rural areas, the main challenge is the high level of capital and operating expenditures incurred by service providers. On the demand side, rural adoption of ICTs in developing countries is curtailed by low availability of complementary public services, such as electricity and education, and by the relative scarcity of locally relevant content. This paper highlight the various aspects of the challenges of setting up a First Mile project in rural Zambia. It takes a rather unique angle in that it introduces the perspectives on the value of academic interventions in a manner that not only addresses some of the complex issues but also accommodates cultural adjustment. The paper introduces the rural internet project in Macha, rural Zambia and demonstrates the impact of such an initiative on setting up not only connectivity in a rural community but also dealing with the challenges that come with this.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.049
Threshold uncertainty score0.456

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.024
GPT teacher head0.261
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2013
Admission routes1
Has abstractyes

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