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A conceptual peer review model for arXiv and other preprint databases
Author(s) -
Wang LingFeng,
Zhan YaQing
Publication year - 2019
Publication title -
learned publishing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.06
H-Index - 34
eISSN - 1741-4857
pISSN - 0953-1513
DOI - 10.1002/leap.1229
Subject(s) - preprint , computer science , ranking (information retrieval) , peer review , peer to peer , matching (statistics) , plan (archaeology) , information retrieval , world wide web , database , data science , political science , history , mathematics , statistics , archaeology , law
A global survey conducted by arXiv in 2016 showed that 58% of arXiv users thought arXiv should have a peer review system. The current opinion is that arXiv should adopt the Community Peer Review model. This paper evaluates and identifies two weak points of Community Peer Review and proposes a new peer review model – Self‐Organizing Peer Review. We propose a model in which automated methods of matching reviewers to articles and ranking both users and articles can be implemented. In addition, we suggest a strategic plan to increase recognition of articles in preprint databases within academic circles so that second generation preprint databases can achieve faster and cheaper publication.

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