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Record W2964424237 · doi:10.6000/1929-7092.2019.08.39

Citizens’ E-Participation at Local Municipal Government in South Africa

2019· article· en· W2964424237 on OpenAlexvenueno aff
Obianuju E. Okeke-Uzodike, Boyie Sabelo Dlamini

Bibliographic record

VenueJournal of Reviews on Global Economics · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsnot available
Fundersnot available
KeywordsLocal governmentGovernment (linguistics)Economic growthBusinessPublic administrationPolitical scienceDevelopment economicsEconomics

Abstract

fetched live from OpenAlex

The emergence of the digital revolution has obligated governments all over the world to be more accessible, effective and accountable in the provision of public services.The South Africa government has implemented various Information and Communications Technology (ICT) enabled platforms in engaging citizens in decision-making, a resultant effect of high world E-government Development Index (EGDI) rating for the country.Irrespective of the rating, the citizens have resorted to public violence as a means of communicating about unsatisfactory service delivery issues.The South African government has continued to witness numerous service delivery protests, resulting in destruction of public property, injury and death of innocent citizens.This article therefore evaluates the application of e-participation in government decision-making, using a three-dimensional framework of e-enabling, e-engaging and e-empowering.The findings show that structural challenges exist that limit the achievement of the good intentions of e-participation.The article relies heavily on existing data and focuses at local municipal level, which is considered the closest point of contact between government and citizens.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.981
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.036
GPT teacher head0.318
Teacher spread0.281 · 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.

Study designNot applicable
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

Citations7
Published2019
Admission routes1
Has abstractyes

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