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Record W3034406398 · doi:10.3138/jsp.51.4.08

The Impact of the COVID-19 Crisis on Scholarly Publishing in China

2020· article· en· W3034406398 on OpenAlexvenueno aff
Yiming Wang, Zhiwu Xu, Qi Zhang

Bibliographic record

VenueJournal of Scholarly Publishing · 2020
Typearticle
Languageen
FieldDecision Sciences
TopicAcademic Publishing and Open Access
Canadian institutionsnot available
Fundersnot available
KeywordsPublishingChinaCoronavirus disease 2019 (COVID-19)Government (linguistics)Scholarly communicationPolitical scienceSubject (documents)Public relationsChinese languageBusinessLibrary scienceLawComputer science

Abstract

fetched live from OpenAlex

This paper describes actions recently taken by the government, scholarly publishers, and researchers to face the COVID-19 challenge in China. By promulgating new policies and funding new programs, the Chinese government at all levels has provided huge support for research on COVID-19. Guided by the new policies, Chinese scholarly book publishers have published 124 new titles on the subject of the coronavirus. Journal publishers have put out numerous calls for papers and launched open access platforms for COVID-19 research. Chinese researchers have produced 2021 English-language papers and 2837 Chinese papers on COVID-19. These activities have the potential to affect scholarly publishing in China and around the world in multiple ways: 1) by establishing a more reasonable academic evaluation system in China; 2) by bringing about a more balanced relationship between Chinese scholarly publishers’ profit motive and their commitment to social welfare; and 3) by altering the communication channels that Chinese researchers use and the publishing choices they make.

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)0.079
metaresearch head score (Gemma)0.708
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Open science, Research integrity
Consensus categoriesMetaresearch, Scholarly communication
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score0.988

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0790.708
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.008
Science and technology studies0.0010.000
Scholarly communication0.3950.441
Open science0.0240.002
Research integrity0.0010.014
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 head0.143
GPT teacher head0.411
Teacher spread0.267 · 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

Citations2
Published2020
Admission routes1
Has abstractyes

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