Single versus Double Blind Reviewing
Single versus Double Blind Reviewing at WSDM 2017

    
    
                
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            Abstract:In this paper we study the implications for conference program committees of using single-blind reviewing, in which committee members are aware of the names and affiliations of paper authors, versus double-blind reviewing, in which this information is not visible to committee members. WSDM 2017, the 10th ACM International ACM Conference on Web Search and Data Mining, performed a controlled experiment in which each paper was reviewed by four committee members. Two of these four reviewers were chosen from a pool of committee members who had access to author information; the other two were chosen from a disjoint pool who did not have access to this information. This information asymmetry persisted through the process of bidding for papers, reviewing papers, and entering scores. Reviewers in the single-blind condition typically bid for 22% fewer papers, and preferentially bid for papers from top institutions. Once papers were allocated to reviewers, single-blind reviewers were significantly more likely than their double-blind counterparts to recommend for acceptance papers from famous authors and top institutions. The estimated odds multipliers are 1.63 for famous authors and 1.58 and 2.10 for top universities and companies respectively, so the result is tangible. For female authors, the associated odds multiplier of 0.78 is not statistically significant in our study. However, a meta-analysis places this value in line with that of other experiments, and in the context of this larger aggregate the gender effect is also statistically significant.
    

    
    
  
      Submission history From: Andrew Tomkins [view email]                  [v1]
        Wed, 1 Feb 2017 23:44:44 UTC (32 KB)
            [v2]
        Tue, 21 Feb 2017 16:11:46 UTC (37 KB)
            [v3]
        Fri, 24 Feb 2017 02:42:28 UTC (37 KB)
            [v4]
        Wed, 19 Apr 2017 01:17:18 UTC (23 KB)
            [v5]
        Fri, 26 May 2017 02:21:13 UTC (24 KB)
    [v6]
        Sun, 8 Oct 2017 00:09:29 UTC (24 KB)
