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On the Predictability of Future Impact in Science
Correctly assessing a scientist's past research impact and potential for future impact is key in recruitment decisions and other evaluation processes. While a candidate's future impact is the main concern for these decisions, most measures only quantify the impact of previous work. Recentl...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3810665/ https://www.ncbi.nlm.nih.gov/pubmed/24165898 http://dx.doi.org/10.1038/srep03052 |
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author | Penner, Orion Pan, Raj K. Petersen, Alexander M. Kaski, Kimmo Fortunato, Santo |
author_facet | Penner, Orion Pan, Raj K. Petersen, Alexander M. Kaski, Kimmo Fortunato, Santo |
author_sort | Penner, Orion |
collection | PubMed |
description | Correctly assessing a scientist's past research impact and potential for future impact is key in recruitment decisions and other evaluation processes. While a candidate's future impact is the main concern for these decisions, most measures only quantify the impact of previous work. Recently, it has been argued that linear regression models are capable of predicting a scientist's future impact. By applying that future impact model to 762 careers drawn from three disciplines: physics, biology, and mathematics, we identify a number of subtle, but critical, flaws in current models. Specifically, cumulative non-decreasing measures like the h-index contain intrinsic autocorrelation, resulting in significant overestimation of their “predictive power”. Moreover, the predictive power of these models depend heavily upon scientists' career age, producing least accurate estimates for young researchers. Our results place in doubt the suitability of such models, and indicate further investigation is required before they can be used in recruiting decisions. |
format | Online Article Text |
id | pubmed-3810665 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Nature Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-38106652013-10-29 On the Predictability of Future Impact in Science Penner, Orion Pan, Raj K. Petersen, Alexander M. Kaski, Kimmo Fortunato, Santo Sci Rep Article Correctly assessing a scientist's past research impact and potential for future impact is key in recruitment decisions and other evaluation processes. While a candidate's future impact is the main concern for these decisions, most measures only quantify the impact of previous work. Recently, it has been argued that linear regression models are capable of predicting a scientist's future impact. By applying that future impact model to 762 careers drawn from three disciplines: physics, biology, and mathematics, we identify a number of subtle, but critical, flaws in current models. Specifically, cumulative non-decreasing measures like the h-index contain intrinsic autocorrelation, resulting in significant overestimation of their “predictive power”. Moreover, the predictive power of these models depend heavily upon scientists' career age, producing least accurate estimates for young researchers. Our results place in doubt the suitability of such models, and indicate further investigation is required before they can be used in recruiting decisions. Nature Publishing Group 2013-10-29 /pmc/articles/PMC3810665/ /pubmed/24165898 http://dx.doi.org/10.1038/srep03052 Text en Copyright © 2013, Macmillan Publishers Limited. All rights reserved http://creativecommons.org/licenses/by-nc-sa/3.0/ This work is licensed under a Creative Commons Attribution-NonCommercial-ShareALike 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/3.0/ |
spellingShingle | Article Penner, Orion Pan, Raj K. Petersen, Alexander M. Kaski, Kimmo Fortunato, Santo On the Predictability of Future Impact in Science |
title | On the Predictability of Future Impact in Science |
title_full | On the Predictability of Future Impact in Science |
title_fullStr | On the Predictability of Future Impact in Science |
title_full_unstemmed | On the Predictability of Future Impact in Science |
title_short | On the Predictability of Future Impact in Science |
title_sort | on the predictability of future impact in science |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3810665/ https://www.ncbi.nlm.nih.gov/pubmed/24165898 http://dx.doi.org/10.1038/srep03052 |
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