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Record W1515765710

The Impacts of Socio-Economic and Cultural Factors on the Network Readiness of Nations: A Focus on the Regions of Africa

2008· article· en· W1515765710 on OpenAlexaff
Princely Ifinedo

Bibliographic record

VenueJournal of the Association for Information Systems · 2008
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsCape Breton University
Fundersnot available
KeywordsExploitInformation and Communications TechnologyIndex (typography)Economic geographyGeographyAssertionDeveloping countryRegional scienceEconomic growthDevelopment economicsComputer scienceEconomicsComputer securityWorld Wide Web
DOInot available

Abstract

fetched live from OpenAlex

The Network Readiness Index (NRI) was developed to measure the propensity for countries (and regions) to exploit information and communications technology (ICT) for development and growth. Much has been written about the influences of economic and non-economic factors on ICT diffusion in developed societies. Studies examining the impact of relevant factors on Africa’s regions’ capabilities to exploit ICT for development are scarce. Moreover, where such studies exist, it is not uncommon for the African continent to be taken as a monolith. This paper argues that the NRI scores for countries across the geographical regions of Africa vary by socio-economic and cultural factors. Hypotheses were developed to test this assertion. The data analysis showed that there are differences across Africa’s regions with respect to the NRI. The data also found relationships between some socio-economic factors, cross-cultural dimensions, and the NRI. The study’s implications for research and policy making are succinctly discussed.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.428
Threshold uncertainty score0.240

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.022
GPT teacher head0.231
Teacher spread0.209 · 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 designNot applicable
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

Citations3
Published2008
Admission routes1
Has abstractyes

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