Double-blind peer-reviewing and inclusiveness in Russian NLP conferences

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Abstract

Double-blind peer reviewing has been proved to be pretty effective and fair way of academic work selection. However, to the best of our knowledge, nobody has yet analysed the effects caused by its introduction at the Russian NLP conferences. We investigate how the double-blind peer reviewing influences gender and location (according to authors’ affiliations) biases and whether it makes two of the conferences under analysis more inclusive. The results show that gender distribution has become more equal for the Dialogue conference, but did not change for the AIST conference. The authors’ location distribution (roughly divided into ‘central’ and ‘not central’) has become more equal for AIST, but, interestingly, less equal for Dialogue.

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APA

Kutuzov, A., & Nikishina, I. (2019). Double-blind peer-reviewing and inclusiveness in Russian NLP conferences. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11832 LNCS, pp. 3–8). Springer. https://doi.org/10.1007/978-3-030-37334-4_1

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