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Record W1634848746 · doi:10.15353/joci.v9i4.3147

Increasing Public Participation in Local Government by Means of Mobile Phones: What do South African Youth Think?

2013· article· en· W1634848746 on OpenAlexvenueno aff
Jean-Paul Van Belle, Kevin Cupido

Bibliographic record

VenueThe Journal of Community Informatics · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)Social capitalThe InternetMobile phoneBusinessPolitical sciencePublic relationsEconomic growthInternet privacyEconomicsEngineeringComputer science

Abstract

fetched live from OpenAlex

Apathy towards political participation is of concern for many countries throughout the world, and for many people political participation means no more than voting in an election. The South African Constitution makes several provisions for public participation but E-government solutions are not suited to the South African context, where fixed-line internet penetration is dramatically lower than that of mobile phones. Mobile phones cut across socio-economic barriers and have changed the way we communicate. They have been used to mobilise people in different parts of the world, more notably those who were passive politically, into action. This research set out to investigate whether using mobile phones to increase participation in local government would be acceptable or not. A mixed-method research was conducted in Cape Town, South Africa, amongst youths between the ages of 18 and 35 who had no access to fixed-line internet from either home or work. Constructs from a modified UTAUT model and Social Capital Theory were used to determine the individual intention to use government mobile service if they were made available. It was found that there is not only great interest in using mobile phones to interact with government mobile services, but also to interact with other members of the community. The ability to report on corruption and service delivery problems was particularly welcome.

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)0.007
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0010.000
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 head0.033
GPT teacher head0.281
Teacher spread0.248 · 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

Citations11
Published2013
Admission routes1
Has abstractyes

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