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Towards a Framework for Government Portal Design

2002· book-chapter· en· W2498005155 on OpenAlexaffabout
Brian Detlor, Kim Finn

Bibliographic record

VenueElectronic Government · 2002
Typebook-chapter
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsEmployment and Social Development CanadaMcMaster University
Fundersnot available
KeywordsImplementationGovernment (linguistics)BusinessE-GovernmentConstruct (python library)Computer scienceProcess managementPublic relationsPolitical scienceKnowledge managementEngineeringSoftware engineeringWorld Wide Web

Abstract

fetched live from OpenAlex

This chapter identifies and describes factors that inhibit and promote successful electronic government portal design. These factors are based on a review of recent research on both electronic government initiatives and corporate portal implementations. The result is a generalized framework for government portal design. To test its viability, the framework is used as a lens to analyze a current case study, specifically a portal project led by the Government of Canada to support Canadian youth citizens. The framework offers an effective preliminary construct by which to focus and pinpoint pertinent issues surrounding government portal design.

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)0.009
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.006
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0040.004
Science and technology studies0.0030.014
Scholarly communication0.0110.015
Open science0.0040.005
Research integrity0.0040.006
Insufficient payload (model declined to judge)0.0060.002

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 head0.029
GPT teacher head0.267
Teacher spread0.239 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations30
Published2002
Admission routes2
Has abstractyes

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