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Record W2082737921 · doi:10.4275/kslis.2003.37.4.151

A Study on the Government Information on the Internet

2003· article· en· W2082737921 on OpenAlexaboutno aff
On-Za Park

Bibliographic record

VenueJournal of the Korean Society for Library and Information Science · 2003
Typearticle
Languageen
FieldSocial Sciences
TopicE-Government and Public Services
Canadian institutionsnot available
Fundersnot available
KeywordsGovernment (linguistics)The InternetBusinessE-GovernmentPublic administrationPolitical scienceWorld Wide WebComputer scienceInformation and Communications Technology

Abstract

fetched live from OpenAlex

정부정보는 한 국가의 국민으로 살아가기 위해서는 누구에게나 필요하다. 인터넷 보급으로 필요한 정부 정보에 보다 쉽게 접근하여 이를 이용할 수 있으며, 특히 전자정부 웹사이트를 통해 필요한 정부정보를 얻을 수 있는 길이 열리고 있다. 본 연구에서는 전자정부 프로젝트를 가장 잘 추진하고 있는 미국, 캐나다, 오스트레일리아의 전자정부에 대하여 조사해 보고, 한국의 전자정부에 대하여 조사하여 전자정부 웹사이트를 통해 보다 유용한 정부 정보를 찾을 수 있는 방법을 모색하여 보았다. Accurate and current government information is essential to any member of that country. Now it has become possible to access government information with greater ease and convenience on the internet. This study examines e-government websites of the U. S. Canada Australia, the leading countries in the e-government project, and provides ways to improve the e-governments website of Korea.

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)0.002
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.006
Science and technology studies0.0040.002
Scholarly communication0.0050.005
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.001

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 head0.020
GPT teacher head0.250
Teacher spread0.230 · 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 designObservational
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

Citations0
Published2003
Admission routes1
Has abstractyes

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