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Does double-blind peer-review reduce bias? Evidence from a top computer science conference

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  • Mengyi Sun
  • Jainabou Barry Danfa
  • Misha Teplitskiy

Abstract

Peer review is widely regarded as essential for advancing scientific research. However, reviewers may be biased by authors' prestige or other characteristics. Double-blind peer review, in which the authors' identities are masked from the reviewers, has been proposed as a way to reduce reviewer bias. Although intuitive, evidence for the effectiveness of double-blind peer review in reducing bias is limited and mixed. Here, we examine the effects of double-blind peer review on prestige bias by analyzing the peer review files of 5027 papers submitted to the International Conference on Learning Representations (ICLR), a top computer science conference that changed its reviewing policy from single-blind peer review to double-blind peer review in 2018. We find that after switching to double-blind review, the scores given to the most prestigious authors significantly decreased. However, because many of these papers were above the threshold for acceptance, the change did not affect paper acceptance decisions significantly. Nevertheless, we show that double-blind peer review may have improved the quality of the selections by limiting other (non-author-prestige) biases. Specifically, papers rejected in the single-blind format are cited more than those rejected under the double-blind format, suggesting that double-blind review better identifies poorer quality papers. Interestingly, an apparently unrelated change - the change of rating scale from 10 to 4 points - likely reduced prestige bias significantly, to an extent that affected papers' acceptance. These results provide some support for the effectiveness of double-blind review in reducing prestige bias, while opening new research directions on the impact of peer review formats.

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  • Mengyi Sun & Jainabou Barry Danfa & Misha Teplitskiy, 2021. "Does double-blind peer-review reduce bias? Evidence from a top computer science conference," Papers 2101.02701, arXiv.org.
  • Handle: RePEc:arx:papers:2101.02701
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    References listed on IDEAS

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    1. Wang, Jian & Veugelers, Reinhilde & Stephan, Paula, 2017. "Bias against novelty in science: A cautionary tale for users of bibliometric indicators," Research Policy, Elsevier, vol. 46(8), pages 1416-1436.
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    3. Carole J. Lee & Cassidy R. Sugimoto & Guo Zhang & Blaise Cronin, 2013. "Bias in peer review," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(1), pages 2-17, January.
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    5. Vincent Larivière & Yves Gingras, 2010. "The impact factor's Matthew Effect: A natural experiment in bibliometrics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(2), pages 424-427, February.
    6. Carole J. Lee & Cassidy R. Sugimoto & Guo Zhang & Blaise Cronin, 2013. "Bias in peer review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 2-17, January.
    7. Blank, Rebecca M, 1991. "The Effects of Double-Blind versus Single-Blind Reviewing: Experimental Evidence from The American Economic Review," American Economic Review, American Economic Association, vol. 81(5), pages 1041-1067, December.
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    Cited by:

    1. Teplitskiy, Misha & Duede, Eamon & Menietti, Michael & Lakhani, Karim R., 2022. "How status of research papers affects the way they are read and cited," Research Policy, Elsevier, vol. 51(4).
    2. Zhentao Liang & Jin Mao & Gang Li, 2023. "Bias against scientific novelty: A prepublication perspective," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 99-114, January.

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