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Measuring risk in science
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DOI：10.1016/j.joi.2023.101426.png)
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Abstract
En 中文
Risk plays a fundamental role in scientific discoveries, and thus it is critical that the level of risk can be systematically quantified. We propose a novel approach to measuring risk entailed in a particular mode of discovery process - knowledge recombination. The recombination of extant knowledge serves as an important route to generate new knowledge, but attempts of recombi-nation often fail. Drawing on machine learning and natural language processing techniques, our approach converts knowledge elements in the text format into high-dimensional vector expres-sions and computes the probability of failing to combine a pair of knowledge elements. Testing the calculated risk indicator on survey data, we confirm that our indicator is correlated with self -assessed risk. Further, as risk and novelty have been confounded in the literature, we examine and suggest the divergence of the bibliometric novelty and risk indicators. Finally, we demonstrate that our risk indicator is negatively associated with future citation impact, suggesting that risk -taking itself may not necessarily pay off. Our approach can assist decision making of scientists and relevant parties such as policymakers, funding bodies, and R & D managers.
Keywords:
Risk
Uncertainty
Novelty
Recombination
Science
Word embedding
Support vector machine
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