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E-Participation and Citizen Relationship Management in Urban Governance

2013· book-chapter· en· W2483681340 on OpenAlexaff
Jim P. Huebner

Bibliographic record

VenueAdvances in electronic government, digital divide, and regional development book series · 2013
Typebook-chapter
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPrivate sectorPublic sectorLaggingContext (archaeology)Scope (computer science)Corporate governanceGovernment (linguistics)BusinessPolitical sciencePublic relationsEconomic growthGeographyEconomics

Abstract

fetched live from OpenAlex

Citizen relationship management (CiRM) is a combination of management approaches and information technologies for improving citizen services and citizen participation used at all levels of government. As an adaptation of private sector customer relationship management (CRM), CiRM is experiencing significant public sector adoption rates globally. However, while private sector CRM has demonstrated significant impact in the private sector, CiRM benefits are limited, and particularly lagging in the area of citizen e-participation in urban governance. This chapter provides an overview of the scope of CiRM functionality, with particular regard to the CRM origins and CiRM extensibilities, to develop a broader perspective of CiRM’s capacity for addressing e-participation. Developing this perspective further, theoretical and methodological approaches to e-participation are presented and evaluated in four categories: generic CiRM participation models, e-government CiRM, democratic CiRM, and strategic CiRM. Further research opportunities are highlighted within the context of emerging organizational, technological, and societal trends.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.865
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.004
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.242
Teacher spread0.229 · 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 designTheoretical or conceptual
Domainnot available
GenreOther

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

Citations3
Published2013
Admission routes1
Has abstractyes

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