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Record W4253228611 · doi:10.17722/ijme.v13i3.1113

Conceptual Framework for the Role of Information and Communication Technology (ICT) in achieving the Sustainable Development Goals

2019· article· en· W4253228611 on OpenAlexvenueno aff
Prabhasara Athurupane, Bhagya Wickramsinghe

Bibliographic record

VenueInternational Journal of Management Excellence · 2019
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyICTSSustainable developmentConceptual frameworkKnowledge managementComponent (thermodynamics)Process managementBusinessConceptual modelManagement scienceComputer sciencePolitical scienceSociologyEconomicsSocial science

Abstract

fetched live from OpenAlex

This paper seeks to evaluate the role of ICT in achieving the sustainable development goals adopted by the United Nations in 2015. While SDGs do not specifically address ICT, or include a goal on development of ICT, as argued (Berleur & Avgerou, 2005)in this paper, it is an underlying element embedded in the very concept of sustainable development rooted in the definition as propounded by the Brundtland Report. The objective of this paper is to evaluate whether there is a possibility to develop a conceptual framework to ground the use of ICTs in achieving SDGs. For this purpose, this research has evaluated the common conceptual frameworks developed by scholars and posits that rather than developing an all-encompassing framework, it is possible to identify certain necessary features for the role of the ICTs in achieving SDGs. This approach enables policy and decision makers to look at the role of ICT as an integral component of socio-economic and environmental decision making and implementation.

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)0.014
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.014
Threshold uncertainty score0.076

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.006
Science and technology studies0.0040.024
Scholarly communication0.0110.011
Open science0.0030.005
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.005
GPT teacher head0.231
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 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

Citations0
Published2019
Admission routes1
Has abstractyes

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