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Open Peer Review: The Current Landscape and Emerging Models

Source Publication
The 17th International Conference on Scientometrics & Informetrics, September 2-5, 2019, Rome, Italy
Date Issued
January 1, 2019
Author(s)
Wolfram, Dietmar
Wang, Peiling  
Park, Hyoungjoo
Permanent URI
https://trace.tennessee.edu/handle/20.500.14382/47911
Abstract

Open peer review (OPR) is an important innovation in the open science movement. OPR can play a significant role in advancing scientific communication by increasing its transparency. Despite the growing interest in OPR, adoption of this innovation since the turn of the century has been slow. This study provides the first comprehensive investigation of OPR adoption, its early adopters and the implementation models used. We identified 174 current OPR journals and analysed their wide-ranging implementations to derive emerging OPR models. The findings suggest that: 1) there has been a steady growth in OPR adoption since 2001 when 38 journals initially adopted OPR; 2) OPR adoption is most prevalent in medicine and the natural sciences; 3) three publishers are responsible for 87% of identified OPR journals; 4) early adopter publishers have implemented different models of OPR resulting in different levels of transparency. Across the variations in OPR implementations, two important factors define the degree of transparency: open identities and open reports. Open identities may include reviewer names and affiliation as well as credentials; open reports may include timestamped review histories consisting of referee reports and author rebuttals. When and where open reports can be accessed are also important factors indicating the OPR transparency level. Dimensions that characterize the observed OPR models are outlined.

Subjects

Open peer review

OPR

Open identities

Open reports

Open science

Transparency

Disciplines
Scholarly Communication
Scholarly Publishing
Recommended Citation
Dietmar Wolfram, Peiling Wang, & Hyoungjoo Park (2019) Open peer review: the current landscape and emerging models. In Proceedings of the The 17th International Conference on Scientometrics & Informetrics (September 2-5, 2019, Rome, Italy)
Submission Type
Post-print
File(s)
Thumbnail Image
Name

ISSIsubmissionRevision_Final.pdf

Size

1.11 MB

Format

Adobe PDF

Checksum (MD5)

0a4dff81a267b4176c5ef1dd8090f793

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