Measuring risk in science
(2023) In Journal of Informetrics 17(3).- Abstract
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 recombination often fail. Drawing on machine learning and natural language processing techniques, our approach converts knowledge elements in the text format into high-dimensional vector expressions 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... (More)
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 recombination often fail. Drawing on machine learning and natural language processing techniques, our approach converts knowledge elements in the text format into high-dimensional vector expressions 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.
(Less)
- author
- Yin, Deyun ; Wu, Zhao and Shibayama, Sotaro LU
- organization
- publishing date
- 2023-08
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Novelty, Recombination, Risk, Science, Support vector machine, Uncertainty, Word embedding
- in
- Journal of Informetrics
- volume
- 17
- issue
- 3
- article number
- 101426
- pages
- 13 pages
- publisher
- Elsevier
- external identifiers
-
- scopus:85164039341
- ISSN
- 1751-1577
- DOI
- 10.1016/j.joi.2023.101426
- language
- English
- LU publication?
- yes
- id
- bb5280fe-df95-42c8-8404-1803e55eec60
- date added to LUP
- 2023-08-20 20:48:38
- date last changed
- 2024-01-20 01:34:40
@article{bb5280fe-df95-42c8-8404-1803e55eec60,
abstract = {{<p>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 recombination often fail. Drawing on machine learning and natural language processing techniques, our approach converts knowledge elements in the text format into high-dimensional vector expressions 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.</p>}},
author = {{Yin, Deyun and Wu, Zhao and Shibayama, Sotaro}},
issn = {{1751-1577}},
keywords = {{Novelty; Recombination; Risk; Science; Support vector machine; Uncertainty; Word embedding}},
language = {{eng}},
number = {{3}},
publisher = {{Elsevier}},
series = {{Journal of Informetrics}},
title = {{Measuring risk in science}},
url = {{http://dx.doi.org/10.1016/j.joi.2023.101426}},
doi = {{10.1016/j.joi.2023.101426}},
volume = {{17}},
year = {{2023}},
}