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Record W2322828552 · doi:10.1177/0169796x14536970

Overcoming the Digital Divide in Developing Countries

2014· article· en· W2322828552 on OpenAlexaff
Frank L. K. Ohemeng, Kwaku Ofosu-Adarkwa

Bibliographic record

VenueJournal of Developing Societies · 2014
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsUnderdevelopmentDigital divideInformation and Communications TechnologyDeveloping countryGovernment (linguistics)AccountabilityThe InternetLanguage changeBusinessICTSEconomic growthPublic relationsService delivery frameworkPublic sectorService (business)Political scienceEconomicsMarketingComputer science

Abstract

fetched live from OpenAlex

The emergence of information communication technologies (ICTs) in developing countries has been hailed as a major step toward a solution to the problem of the underdevelopment of many of them. Obstacles such as corruption, delays in service delivery, lack of public sector accountability, and so on can many believe be overcome with ICT: particularly, the Internet and cell or mobile phones. Consequently, governments in these countries continue to expend a lot of their meager resources on ensuring the effective development and use of ICTs. In spite of this, a major problem that these countries face is what has been described as the digital divide. The purpose of this article is, therefore, to examine the government’s attempt to address this problem including how the problem has been defined, the steps that are being taken to heal it, the implied challenges, if any, facing the government, and how it can address these challenges.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0050.006
Scholarly communication0.0080.007
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.001

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 head0.012
GPT teacher head0.237
Teacher spread0.224 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations34
Published2014
Admission routes1
Has abstractyes

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