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Record W3142907402 · doi:10.29173/iasl7558

Bridging the Digital Divide through Schools

2021· article· en· W3142907402 on OpenAlexvenueno aff
Stephen M. Mutula

Bibliographic record

VenueIASL Annual Conference Proceedings · 2021
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsDigital divideThe InternetBridging (networking)Public relationsEntertainmentEquity (law)Internet privacyInternet accessInformation and Communications TechnologyPhenomenonInformation AgePolitical scienceSociologyEconomic growthBusinessComputer scienceWorld Wide WebEconomicsComputer security

Abstract

fetched live from OpenAlex

The Internet has become increasingly an important source of information for pupils in learning, entertainment, sharing and exchanging experiences with other peers, meeting with adults and learning about other cultures. The Internet is a major force in the lives of school children and is no doubt having a tremendous influence on their reading habits and information seeking behaviour. With increased Internet connectivity however, concerns are being raised about the widening imbalances of access to ICTs between north information haves and the information have nots countries. This imbalance known as the digital divide has implications in terms of equity access to quality education in an electronic age. In Africa there are limited programmes that address in particular how schools can be equipped to benefit from the digital age and at the same time be used as an important instrument to bridge the digital divide. The concept digital divide was coined to describe the imbalance in access to information and communication technologies by different communities of people between different countries of the world. This phenomenon is today known to exist within individual countries, cities and even communities. Today schools are being seen as one of the most salient infrastructure that can be used to bridge the digital divide in our midst. This paper looks at developments in Africa aimed at bridging the digital divide through schools.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.329
Threshold uncertainty score0.823

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 communication0.0010.001
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 head0.019
GPT teacher head0.243
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 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
Published2021
Admission routes1
Has abstractyes

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