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E-Democracy and Local Government - Dashed Expectations

2007· book-chapter· en· W2502120979 on OpenAlexaffabout
Peter Smith

Bibliographic record

VenueIGI Global eBooks · 2007
Typebook-chapter
Languageen
FieldSocial Sciences
TopicPolitical Systems and Governance
Canadian institutionsAthabasca University
Fundersnot available
KeywordsDemocracyPoliticsPolitical scienceICTSRepresentative democracyPublic administrationDirect democracyLocal governmentDominance (genetics)Political economyInformation and Communications TechnologySociologyLaw

Abstract

fetched live from OpenAlex

This article examines the impact of information and communications technologies (ICTs) on electronic democracy at the local government level. It concentrates on measures taken by local governments in the United States, Canada, and the United Kingdom to transform their relationship to citizens by means of e-democracy. The emphasis on democracy is particularly important in an era when governments at all levels are said to be facing a democratic deficit (Hale, Musso, & Weare, 1999; Juillet & Paquet, 2001). Yet, as this article argues by means of an examination of the available evidence in the United States, Canada, and the United Kingdom, e-democracy has failed to deepen democracy at the local level, this at a time when local government is said to be becoming more important in people’s lives (Mälkiä & Savolainen, 2004). The first part of the article briefly summarizes the arguments on behalf of the growing importance of the city as a major locus of economic and political activity. It then discusses how e-democracy relates to e-government in general. Next, it discusses the normative relationship between two models of democracy and ICTs. The article then reviews the evidence to date of e-democracy at the local level of government in the aforementioned countries. Finally, it discusses why e-democracy has not lived up to expectations highlighting the dominance of neo-liberalism.

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 categoriesnone
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 score0.615
Threshold uncertainty score0.998

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 communication0.0000.000
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 head0.023
GPT teacher head0.293
Teacher spread0.270 · 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 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
Published2007
Admission routes2
Has abstractyes

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