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使用睡美人与王子对延迟识别进行大规模分析。

Large-scale analysis of delayed recognition using sleeping beauty and the prince.

作者信息

Miura Takahiro, Asatani Kimitaka, Sakata Ichiro

机构信息

Department of Technology Management for Innovation, School of Engineering, The University of Tokyo, 7-3-1 Faculty of Engineering Bldg 3, Hongo, Bunkyo, Tokyo, 113-8656 Japan.

出版信息

Appl Netw Sci. 2021;6(1):48. doi: 10.1007/s41109-021-00389-0. Epub 2021 Jun 30.

Abstract

Delayed recognition in which innovative discoveries are re-evaluated after a long period has significant implications for scientific progress. The quantitative method to detect delayed recognition is described as the pair of Sleeping Beauty (SB) and its Prince (PR), where SB refers to citation bursts and its PR triggers SB's awakeness calculated based on their citation history. This research provides the methods to extract valid and large SB-PR pairs from a comprehensive Scopus dataset and analyses how PR discovers SB. We prove that the proposed method can extract long-sleep and large-scale SB and its PR best covers the previous multi-disciplinary pairs, which enables to observe delayed recognition. Besides, we show that the high-impact SB-PR pairs extracted by the proposed method are more likely to be located in the same field. This indicates that a hidden SB that your research can awaken may exist closer than you think. On the other hand, although SB-PR pairs are fat-tailed in Beauty Coefficient and more likely to integrate separate fields compared to ordinary citations, it is not possible to predict which citation leads to awake SB using the rarity of citation. There is no easy way to limit the areas where SB-PR pairs occur or detect it early, suggesting that researchers and administrators need to focus on a variety of areas. This research provides comprehensive knowledge about the development of scientific findings that will be evaluated over time.

摘要

延迟识别是指创新发现经过很长一段时间后才被重新评估,这对科学进步具有重大影响。检测延迟识别的定量方法被描述为睡美人(SB)及其王子(PR)的组合,其中SB指的是引文爆发,其PR根据其引文历史计算触发SB的觉醒。本研究提供了从全面的Scopus数据集中提取有效且大量的SB - PR对的方法,并分析了PR如何发现SB。我们证明,所提出的方法能够提取长睡眠且大规模的SB,其PR能最好地涵盖先前的多学科对,从而能够观察到延迟识别。此外,我们表明,通过所提出的方法提取的高影响力SB - PR对更有可能位于同一领域。这表明你研究中能够唤醒的隐藏SB可能比你想象的更近。另一方面,虽然SB - PR对在美系数上呈厚尾分布,并且与普通引文相比更有可能整合不同领域,但无法利用引文的稀有性来预测哪条引文会唤醒SB。没有简单的方法来限制SB - PR对出现的领域或早期检测到它,这表明研究人员和管理人员需要关注各种领域。这项研究提供了关于科学发现随着时间推移将如何被评估的全面知识。

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