Data-driven classification of the certainty of scholarly assertions.

ABSTRACT: The grammatical structures scholars use to express their assertions are intended to convey various degrees of certainty or speculation. Prior studies have suggested a variety of categorization systems for scholarly certainty; however, these have not been objectively tested for their validity, particularly with respect to representing the interpretation by the reader, rather than the intention of the author. In this study, we use a series of questionnaires to determine how researchers classify various scholarly assertions, using three distinct certainty classification systems. We find that there are three distinct categories of certainty along a spectrum from high to low. We show that these categories can be detected in an automated manner, using a machine learning model, with a cross-valid

SUBMITTER: Prieto M 

PROVIDER: S-EPMC7182025 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature