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Record W1987884527 · doi:10.5539/jsd.v3n4p165

Towards Effective Use of ICTS and Traditional Media for Sustainable Rural Transformation in Africa

2010· article· en· W1987884527 on OpenAlexvenueno aff
Akpomuvie Orhioghene Benedict

Bibliographic record

VenueJournal of Sustainable Development · 2010
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)ICTSSustainable developmentPublic relationsEconomic growthBusinessProcess (computing)Information and Communications TechnologyPolitical scienceEconomics

Abstract

fetched live from OpenAlex

Development programmes initiated by governments in parts of Africa aimed at improving the living standard of the people, have either achieved minimal success or failed because of the negative attitude of the people. One issue that has therefore dominated discussions among development agencies and initiators in Africa is that of dealing with the people’s attitudes and responses to issues of development. This paper is therefore geared towards exploring ways in which ICTs and traditional media could become effective tools in the campaigns and mobilization for the adoption of innovations, which is central to rural development in Africa. The method of content analysis using review of existing document such as books, journals, periodicals, case record and others documented by government, individuals and organization s was utilized. The findings revealed that the majority of the people in Africa lived in the rural areas and were not abreast with information about government programmes aimed at improving them. Apart from acknowledging the basic challenges confronting policy makers, development communicators and other stakeholders in the development process, the paper recommends among others, that governments in Africa should create the enabling environment for the ICTS to be effectively used and synergized with traditional media in achieving development communication goals at all levels.

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)0.000
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 score0.520
Threshold uncertainty score0.413

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
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 head0.013
GPT teacher head0.214
Teacher spread0.201 · 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

Citations6
Published2010
Admission routes1
Has abstractyes

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