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Record W1990193931 · doi:10.1353/scp.2007.0001

Measures to Shorten Publication Lags for Journals in China

2006· article· en· W1990193931 on OpenAlexvenueno aff
Fengnian Wang

Bibliographic record

VenueJournal of Scholarly Publishing · 2006
Typearticle
Languageen
FieldArts and Humanities
TopicPublishing and Scholarly Communication
Canadian institutionsnot available
Fundersnot available
KeywordsTime lagPublishingChinaCompetition (biology)Quality (philosophy)LagValue (mathematics)Key (lock)Lag timeComputer scienceAdvertisingBusinessPolitical scienceLawComputer security

Abstract

fetched live from OpenAlex

Because of the lengthy time taken for authors to revise and experts to evaluate, most periodicals in China suffer from publication lag. In the intense competition of the periodical publishing industry, publication lag has become one of the key factors in evaluating the quality of periodicals, as well as the academic value of articles. This article discusses ways to shorten the time from submission to publication, such as using electronic technologies and improving communication between authors and editors.

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)0.013
metaresearch head score (Gemma)0.011
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesScholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.359
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0130.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.000
Science and technology studies0.0010.000
Scholarly communication0.1220.150
Open science0.0020.000
Research integrity0.0000.002
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 head0.066
GPT teacher head0.277
Teacher spread0.210 · 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; both teacher heads agree on what is shown here.

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

Citations4
Published2006
Admission routes1
Has abstractyes

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