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The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review.

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Title: The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review.
Authors: Su, Buxin1 (AUTHOR), Zhang, Jiayao1 (AUTHOR), Collina, Natalie2 (AUTHOR), Yan, Yuling3 (AUTHOR), Li, Didong4 (AUTHOR), Cho, Kyunghyun5 (AUTHOR), Fan, Jianqing6 (AUTHOR), Roth, Aaron2 (AUTHOR), Su, Weijie1 (AUTHOR) suw@wharton.upenn.edu
Source: Journal of the American Statistical Association. May2025, p1-12. 12p. 6 Illustrations.
Database: Business Source Ultimate
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An: 185645532
AccessLevel: 2
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
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  Data: The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review.
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  Data: <searchLink fieldCode="JN" term="%22Journal+of+the+American+Statistical+Association%22">Journal of the American Statistical Association</searchLink>. May2025, p1-12. 12p. 6 Illustrations.
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=bsu&AN=185645532
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      – Type: doi
        Value: 10.1080/01621459.2025.2510006
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      – Code: eng
        Text: English
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      Pagination:
        PageCount: 12
        StartPage: 1
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      – TitleFull: The ICML 2023 Ranking Experiment: Examining Author Self-Assessment in ML/AI Peer Review.
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              Text: May2025
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              Y: 2025
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