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Record W2089575233 · doi:10.1108/14684520610716199

Korean Electronic Site License Initiative: archiving of electronic journals

2006· article· en· W2089575233 on OpenAlexaff
Eun G. Park, Ho Nam Choi

Bibliographic record

VenueOnline Information Review · 2006
Typearticle
Languageen
FieldArts and Humanities
TopicDigital and Traditional Archives Management
Canadian institutionsMcGill University
Fundersnot available
KeywordsLicenseMetadataComputer scienceDigital libraryOriginalityWorld Wide WebSelection (genetic algorithm)Electronic libraryArchitectureElectronic documentElectronic publishingEngineering managementEngineeringThe InternetPolitical scienceGeography

Abstract

fetched live from OpenAlex

Purpose The purpose of the paper is to present a progress report on the building of a digital archive of electronic journals by the Korean Electronic Site License Initiative (KESLI) of the National Digital Science Library in Korea. Design/methodology/approach The research involves identifying a system architecture, testing its prototype, deciding journal selection criteria and priority publisher selection criteria, and defining metadata sets. Findings The paper finds that production system and archiving system models are being implemented and tested. Implementation of the delivery system module is in progress. Preservation strategies will be identified. Practical implications The KESLI electronic journal digital archive project is proceeding toward completion of its tasks by the end of 2007. Originality/value The observations and lessons in the paper provide insights into building a digital archive of electronic journals that can be used as a basis for building similar digital archives in other countries.

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)0.012
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.994
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.007
Science and technology studies0.0020.001
Scholarly communication0.0060.007
Open science0.0020.005
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0060.003

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 head0.244
Teacher spread0.224 · 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.

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

Citations7
Published2006
Admission routes1
Has abstractyes

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