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  5. Grade Language Heterogeneity in Simulation Models of Peer Review
 
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Grade Language Heterogeneity in Simulation Models of Peer Review

Author(s)
Feliciani, Thomas  
Moorthy, Ramanathan  
Lucas, Pablo  
Shankar, Kalpana  
Uri
http://hdl.handle.net/10197/12034
Date Issued
2020-06-30
Date Available
2021-03-11T13:15:21Z
Abstract
Simulation models have proven to be valuable tools for studying peer review processes. However, the effects of some of these models’ assumptions have not been tested, nor have these models been examined in comparative contexts. In this paper, we address two of these assumptions which go in tandem: (1) on the granularity of the evaluation scale, and (2) on the homogeneity of the grade language (i.e. whether reviewers interpret evaluation grades in the same fashion). We test the consequences of these assumptions by extending a well-known agent-based model of author and reviewer behaviour with discrete evaluation scales and reviewers’ interpretation of the grade language. In this way, we compare a peer review model with a homogeneous grade language, as assumed in most models of peer review, with a more psychologically realistic model where reviewers interpret the grades of the evaluation scale heterogeneously. We find that grade language heterogeneity can indeed affect the predictions of a model of peer review.
Sponsorship
Science Foundation Ireland
Type of Material
Journal Article
Publisher
University of Surrey
Journal
Journal of Artificial Societies and Social Simulation
Volume
23
Issue
3
Copyright (Published Version)
2020 JASSS
Subjects

Peer review

Grade language

Agent-based modeling

DOI
10.18564/jasss.4284
Language
English
Status of Item
Peer reviewed
ISSN
1460-7425
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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Owning collection
Information and Communication Studies Research Collection
Mapped collections
Geary Institute Research Collection•
Sociology Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
All other content is subject to copyright.

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