An improved practical approach to forecasting exceptional growth in research

Bibliographic Data

ID21255831
AuthorsKevin W Boyack (0000-0001-7814-8951, SciTech Strategies (United States)), Richard Klavans (0000-0002-9118-8955, SciTech Strategies (United States))
Year2022
Volume3
Issue3
Pages672-693
Publication date2022-11-01
Open AccessYes
TypeARTICLE
VenueQuantitative Science Studies (JOURNAL)
Journal identifiersISSN: 2641-3337 • E-ISSN: 2641-3337
PublisherMIT Press (PUBLISHER • US)
DOI10.1162/qss_a_00202
OpenAlexW4292386868
LanguageEN
Citations received2
References cited20

The accurate forecasting of exceptional growth in research areas has been an extremely difficult problem to solve. In a previous study we introduced an approach to forecasting which research clusters in a global model of the scientific literature would have an annual growth rate of 8% annually over a 3-year period. In this study we (a) introduce a much more robust method of creating and updating global models of research, (b) introduce new indicators based on author publication patterns, (c) test a much larger set (81) of indicators to forecast exceptional growth, and (d) expand the forecast horizon from 3 to 4 years. Forecast accuracy increased dramatically (threat score increased from 20 to 32) from our previous study. Most of this gain is surprisingly due to the advances in model robustness rather than the indicators used for forecasting. We also provide evidence that most indicators (including popular network indicators) do not improve the ability to forecast growth in research above the baseline provided by indicators associated with the vitality of a research cluster

Econometrics · Vitality · Complex Systems and Time Series Analysis · Computer Science · Ecosystem dynamics and resilience · Mathematics · scientometrics and bibliometrics research

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