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What Kinds of Organisations do We Want to Build in Africa with Information Communication Technology?

2011· book-chapter· en· W4254393963 on OpenAlexaboutno aff
Rembrandt Klopper

Bibliographic record

VenueIGI Global eBooks · 2011
Typebook-chapter
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyDisadvantagedOrder (exchange)BusinessQuarter (Canadian coin)Information technologyPolitical sciencePublic relationsEngineeringEconomic growthEconomicsGeography

Abstract

fetched live from OpenAlex

In the first half of this contribution, the author focuses on what information communication technology (ICT) could be implemented in Africa in order to integrate the continent into the emerging global culture and associated economy. In the second half, he assesses the state of ICT implementation in Africa. The emergence of worldwide information and communications technology (ICT) networks in the last quarter of the 20th century has steadily effected vast and permanent changes with regard to how people in free market open societies communicate, work, do business, and spend their leisure time. In spite of the recent bursting of the dot com bubble and increasing strains experienced in the ICT manufacturing sector, advances in information technology and telecommunications (ICT) will continue to reshape the major institutions of society in the 21st century. This ought to lead to a more efficient way of life for at least some people. However, it is not clear whether this “progress” will actually be satisfactory for all. There are many more facets to the application of ICT than simple business efficiency. This chapter asks, “after 50 years of ICT, what kind of society do we want to create for ourselves, and what level of choices are available to individuals and corporate entities?” As was pointed out at the EU meeting in Lisbon in 2000, we need to be particularly aware of the potential for ICT to improve the lives of those who are disadvantaged.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.820

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 head0.013
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 designTheoretical or conceptual
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

Citations1
Published2011
Admission routes1
Has abstractyes

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