Effectiveness of anonymization in double-blind review

descriptionPublicationkeyboard_double_arrow_right Article , Preprint , Other literature type 23 May 2018Embargo end date: 01 Sep 2017 English Publisher:Association for Computing Machinery (ACM)Journal:Communications of the ACM, volume 61, pages 30-33 (issn: 0001-0782, eissn: 1557-7317,

Copyright policy )Funded by:NSF | SHF: Small: Collaborative..., NSF | CPS: Synergy: Physically-..., DFG | unidentified +1 projects

Authors: Claire Le Goues; Yuriy Brun; Sven Apel; Emery D. Berger; Sarfraz Khurshid; Yannis Smaragdakis;

doi: 10.1145/3208157 , 10.48550/arxiv.1709.01609

arXiv: 1709.01609

Effectiveness of anonymization in double-blind review

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Abstract

Assessing the effectiveness of anonymization in the review process.

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Keywords

Software Engineering (cs.SE), FOS: Computer and information sciences, Computer Science - Software Engineering, Computer Science - General Literature, Computer Science - Digital Libraries, Digital Libraries (cs.DL), General Literature (cs.GL)

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	selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	26
	popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.	Top 10%
	influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).	Top 10%
	impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.	Top 10%