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Information Technology Usage in Nigeria

2005· book-chapter· en· W96056801 on OpenAlexaff
Isola Ajiferuke, Wole Michael Olatokun

Bibliographic record

VenueIGI Global eBooks · 2005
Typebook-chapter
Languageen
FieldComputer Science
TopicEducational Technology and Optimization
Canadian institutionsWestern University
Fundersnot available
KeywordsFirmwareComputer scienceContext (archaeology)SoftwareTerm (time)Information technologyElectronicsAutomated information systemInformation systemControl (management)Information transmissionTelecommunicationsManagement information systemsInformation managementMicrocodeInformation handlingEngineeringKnowledge managementDatabaseOperating systemComputer networkElectrical engineering

Abstract

fetched live from OpenAlex

The term information technology (IT) came into common use in the late 1980s, supplanting earlier terms such as electronic data processing systems (EDP), management information systems (MIS), and office systems (IS). Oliver, E. C., Chapman, R. J. and French, C. S. (1990) defined IT as the technology which supports activities involving the creation, storage, manipulation and communication of information (principally computing, electronics and electronic communications) together with their related methods, management and applications. Such an all-embracing term is clearly open to a number of interpretations depending on in which context it is used. In some contexts, the term “information technology” means computers, ancillary equipment, software and firmware (hardware) and similar procedures, services (including support services) and related resources. Also, it includes any equipment or interconnected system or subsystem of equipment that is used in the automatic acquisition, storage, manipulation, management, movement, control, display, switching, interchange, transmission or reception of data or information.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.073

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.010
Science and technology studies0.0040.002
Scholarly communication0.0060.004
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0220.005

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 head0.009
GPT teacher head0.234
Teacher spread0.226 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2005
Admission routes1
Has abstractyes

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