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arXiv 1901.06246cs.CYcs.DLcs.LGstat.ML
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避免同行评议过程中的公共资源悲剧

Avoiding a Tragedy of the Commons in the Peer Review Process

Google AI(谷歌人工智能(Google AI))

D Sculley, Jasper Snoek, Alex Wiltschko

摘要:

同行评审是科学出版的基础,评审工作长期以来一直被视为专业服务的基石。然而,机器学习领域的大规模增长使这一社区利益受到压力,威胁到有效审查过程的可持续性和该领域的整体进展。在这份立场文件中,我们认为,通过强调这项服务的专业方面,可以避免公地悲剧的结果。特别是,我们提出了一个规则,以保持审查员的审查质量的客观标准。反过来,我们也建议给予审查人员适当的激励。作为一种可能的激励措施,我们探讨了在每次审查的基础上进行经济补偿的想法。我们提出了合理的融资模式和长期效果的思考。

英文摘要:

Peer review is the foundation of scientific publication, and the task of reviewing has long been seen as a cornerstone of professional service. However, the massive growth in the field of machine learning has put this community benefit under stress, threatening both the sustainability of an effective review process and the overall progress of the field. In this position paper, we argue that a tragedy of the commons outcome may be avoided by emphasizing the professional aspects of this service. In particular, we propose a rubric to hold reviewers to an objective standard for review quality. In turn, we also propose that reviewers be given appropriate incentive. As one possible such incentive, we explore the idea of financial compensation on a per-review basis. We suggest reasonable funding models and thoughts on long term effects.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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