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Record W2213625183 · doi:10.5539/jel.v5n1p1

Impact of the Digital Divide on Computer Use and Internet Access on the Poor in Nigeria

2015· article· en· W2213625183 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Education and Learning · 2015
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetDigital divideInternet accessPovertyLocal government areaGovernment (linguistics)Computer literacyOgun stateLocal governmentBusinessInternet privacyEconomic growthPolitical scienceComputer scienceWorld Wide WebPublic administrationEconomics

Abstract

fetched live from OpenAlex

<p>We recruited 20 community members in Ido Local Government Area, Oyo state and Yewa Local Government Area, Ogun state in Nigeria to explore experiences and perceptions of Internet access and computer use. Face-to-face interviews were conducted using open-ended questions to collect qualitative data regarding accessibility of information and communication technology. Twenty low-income community members volunteered to participate in the study. The results centered around affordability of computers and Internet access, exposure to information on the Internet, increasing access to the Internet, training on computer use, benefits for job searching, and networking. The results indicated the lack of Internet access, affordability of computers and Internet usage, poverty, lack of computer skills, and poor infrastructures were contributors to the digital divide.</p>

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.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.094
Threshold uncertainty score0.145

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.313
Teacher spread0.276 · 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