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Record W2088975618 · doi:10.1109/hicss.2013.438

Organizational Requirements for Building Up National E-Government Infrastructures in Federal Settings

2013· article· en· W2088975618 on OpenAlexaboutno aff
Marianne Fraefel, Thomas Selzam, Reinhard Riedl

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)USableDimension (graph theory)BusinessKnowledge managementDigital governmentProcess managementCorporate governanceE-GovernmentPublic administrationPublic relationsPolitical scienceComputer scienceFinance

Abstract

fetched live from OpenAlex

The present paper focuses on the interplay between the organizational dimension of e-government and the development of national e-government infrastructures. The discussion is aimed at clarifying whether and how a decentralized vs. central development of re-usable basic services raises different requirements with regard to establishing inter-organizational arrangements and coordination. Current challenges in the development of decentralized, federal Switzerland are compared to other federal and non-federal countries, based on document analysis and interviews with e-government experts in Switzerland, selected European countries and Canada. Against this background, an organizational framework is developed that is aimed at overcoming common obstacles for developing an integrated e-government approach across national tiers in Switzerland. The cross-country comparison reveals considerable similarity regarding pressing challenges. The framework may therefore be suited as a theoretical model for further analyses on the guidance, design and governance of e-government infrastructures. Practitioners might apply it as an analytical tool.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.024
Threshold uncertainty score0.050

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0050.003
Scholarly communication0.0070.004
Open science0.0010.004
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.019
GPT teacher head0.302
Teacher spread0.283 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations12
Published2013
Admission routes1
Has abstractyes

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