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Record W1556125009 · doi:10.15353/joci.v11i2.2840

Embedding Digital Advantage: A Five-Stage Maturity Model for Digital Communities

2015· article· en· W1556125009 on OpenAlexvenueno aff
Andy Williamson

Bibliographic record

VenueThe Journal of Community Informatics · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovative Approaches in Technology and Social Development
Canadian institutionsnot available
Fundersnot available
KeywordsMaturity (psychological)Information and Communications TechnologyCapability Maturity ModelProcess (computing)Knowledge managementDigital divideAuditInformation technologyPublic relationsBusinessComputer sciencePolitical scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Information and Communications Technology (ICT) has the potential to offer citizens new ways with which to engage in the process of democracy. It is not sufficient that communities and citizens have access to ICT for the digital divide to be bridged; such communities need to also become literate in the new technologies. Resources need to be made available that are useful, interesting and relevant. Ultimately, communities need to be empowered to become more than receivers of information and services via technology; they must become producers of new knowledge and information. Such publications are then able to represent a community’s unique viewpoint to a wider audience. This paper describes a five stage model for community ICT engagement and maturity. This model is non-linear and temporal and can be used as an audit of current community technology capability for assessing maturity and for establishing clear milestones within a community ICT framework. Such a model is useful for assessing and developing eDemocracy issues within individual groups and communities and as a way of mapping progress within a wider community, city or regional setting.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.147
Threshold uncertainty score0.576

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.004
Open science0.0010.001
Research integrity0.0000.001
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 head0.074
GPT teacher head0.295
Teacher spread0.221 · 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 designQualitative
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

Citations2
Published2015
Admission routes1
Has abstractyes

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