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Record W2886092291 · doi:10.1108/ijpl-05-2018-0025

Citizen engagement in South Africa: the case Prince Albert

2018· article· en· W2886092291 on OpenAlexaboutno aff
Vinitha Siebers

Bibliographic record

VenueInternational Journal of Public Leadership · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Issues in South Africa
Canadian institutionsnot available
Fundersnot available
KeywordsPublic engagementCommunity engagementPublic relationsOriginalityValue (mathematics)Political scienceConstruct (python library)Local governmentCivic engagementUnit (ring theory)ConstitutionGovernment (linguistics)Social engagementSociologyPublic administrationQualitative researchPsychologyPoliticsSocial scienceComputer scienceLawMathematics education

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to gain insight in how South African local governments organize citizen engagement. The new South African constitution provides ways to construct and implement citizen engagement at local level. However, understanding citizen engagement at local level is still a challenge and municipalities search for proper structures and mechanisms to organize citizen engagement efficiently. Design/methodology/approach Interviews with different municipal actors were analyzed using a single case study method. As a primary unit of analysis, a specific project in which citizen engagement is organized was used. In addition, document analysis and a focus group were used to deepen understanding. Findings The findings reveal that citizen engagement is a viable strategy to identify the needs of the community if facilitated by a third party and that learning leadership is important when organizing citizen engagement. Originality/value The value of this research is the exploration of the citizen engagement process. It sheds light on the conditions that play a role when a local government organizes citizen engagement. As local governments search for ways to effectively organize and structure citizen engagement, insight into these conditions is helpful.

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)0.005
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.827
Threshold uncertainty score0.761

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.214
GPT teacher head0.367
Teacher spread0.153 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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