On the Predictability of Future Impact in Science View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2013-10-29

AUTHORS

Orion Penner, Raj K. Pan, Alexander M. Petersen, Kimmo Kaski, Santo Fortunato

ABSTRACT

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. More... »

PAGES

3052

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/srep03052

DOI

http://dx.doi.org/10.1038/srep03052

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1019091582

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/24165898


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