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Record W1603920370 · doi:10.15353/joci.v10i2.2651

ICT for sustainable development: an example from Cambodia

2013· article· en· W1603920370 on OpenAlexvenueno aff
Helena Grunfeld

Bibliographic record

VenueThe Journal of Community Informatics · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsnot available
Fundersnot available
KeywordsInformation and Communications TechnologyEmpowermentBusinessAdaptation (eye)Sustainable developmentFunction (biology)Economic growthConceptual frameworkICTSKnowledge managementPublic relationsPolitical scienceEconomicsSociologyComputer science

Abstract

fetched live from OpenAlex

Can telecentres get a new lease of life, facilitating climate change adaptation and mitigation? Having been widely promoted as an innovative way of bringing information and communication technologies (ICTs) to developing countries since the 1990s, the efficacy of and need for such centres has been questioned more recently, with the attention of ICT for development (ICT4D) shifting towards market-led mobile services. But it is doubtful that sufficient account has been taken of the many, non-ICT roles played by such centres, whether in fostering education, empowerment, or greater environmental awareness. Through a conceptual framework informed by the capability approach, research discovered that iREACH, a Cambodian telecentre initiative had encouraged farming practices compatible with sustainable development. The paper suggests that telecentres might hold some promise in this area, and calls for greater focus on this potential function of such facilities, both in research, policy 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)0.001
metaresearch head score (Gemma)0.001
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: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.137

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0030.002
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.046
GPT teacher head0.241
Teacher spread0.195 · 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

Citations5
Published2013
Admission routes1
Has abstractyes

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