TY - GEN
T1 - A practical approach to constructing triple-blind review process with maximal anonymity and fairness
AU - Jung, Jisoo
AU - Kim, Joo Im
AU - Yoon, Ji Won
PY - 2017
Y1 - 2017
N2 - Most journals and conferences adopt blind review process to ensure fairness through anonymization. Although the identity of an author is blinded in a manuscript, information about the author is known to the system when an account is created for submission. So, Information leak or the abuse from journal editor, who is able to access this information, could discredit the review process. Therefore, the triple-blind review process has been proposed to maximize anonymity through blinding the author, reviewer and also the editor. However, it has not been widely used compared to single- and double-blind review processes because there is difficulty in selecting the reviewers when the author is not known to the editor. In this paper, we propose a novel scheme to select the adequate reviewers in the triple-blind review process without any disclosure of author information to even the editor. This is done by using machine learning classification and a conflict of interest measuring method.
AB - Most journals and conferences adopt blind review process to ensure fairness through anonymization. Although the identity of an author is blinded in a manuscript, information about the author is known to the system when an account is created for submission. So, Information leak or the abuse from journal editor, who is able to access this information, could discredit the review process. Therefore, the triple-blind review process has been proposed to maximize anonymity through blinding the author, reviewer and also the editor. However, it has not been widely used compared to single- and double-blind review processes because there is difficulty in selecting the reviewers when the author is not known to the editor. In this paper, we propose a novel scheme to select the adequate reviewers in the triple-blind review process without any disclosure of author information to even the editor. This is done by using machine learning classification and a conflict of interest measuring method.
KW - Artificial neural network
KW - Author identification
KW - Blind review process
KW - Conflict Of Interest
KW - Multinomial naive Bayes
UR - http://www.scopus.com/inward/record.url?scp=85017577245&partnerID=8YFLogxK
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U2 - 10.1007/978-3-319-56549-1_17
DO - 10.1007/978-3-319-56549-1_17
M3 - Conference contribution
AN - SCOPUS:85017577245
SN - 9783319565484
VL - 10144 LNCS
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 198
EP - 209
BT - Information Security Applications - 17th International Workshop, WISA 2016, Revised Selected Papers
PB - Springer Verlag
T2 - 17th International Workshop on Information Security Applications, WISA 2016
Y2 - 25 August 2016 through 25 August 2016
ER -