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Record W2910361931 · doi:10.1002/isd2.12072

Identifying an analytical tool to assess the readiness of aid information and communication technology projects

2019· article· en· W2910361931 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Electronic Journal of Information Systems in Developing Countries · 2019
Typearticle
Languageen
FieldEngineering
TopicICT Impact and Policies
Canadian institutionsAthabasca University
Fundersnot available
KeywordsInformation and Communications TechnologyICTSBusinessOrder (exchange)Knowledge managementSustainable developmentProcess managementProject managementEngineeringComputer sciencePolitical scienceFinanceSystems engineering

Abstract

fetched live from OpenAlex

Abstract It has been established that information and communication technologies (ICTs) empower the informal sector. However, according to a study, a number of barriers such as affordability, availability, and access lead to the informal sector being a low‐tech environment. Thus, aid agencies and African governments have made notable efforts to promote the spread and use of ICT in Africa in order to support sustainable development in African countries. These initiatives are managed on a project basis. Unfortunately, the success rate of development aid projects is relatively low. Several explanations have been given for these failures. We performed a conceptual analysis where we identify the important drivers that should be considered when assessing the likelihood of success of these projects, namely, project readiness, before such projects are implemented. The analytical framework that is proposed will contribute to the success of ICT projects.

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.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.852
Threshold uncertainty score0.230

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.017
GPT teacher head0.280
Teacher spread0.263 · 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