  Certainty and uncertainty are fundamental to science communication. Hedges
have widely been used as proxies for uncertainty. However, certainty is a
complex construct, with authors expressing not only the degree but the type and
aspects of uncertainty in order to give the reader a certain impression of what
is known. Here, we introduce a new study of certainty that models both the
level and the aspects of certainty in scientific findings. Using a new dataset
of 2167 annotated scientific findings, we demonstrate that hedges alone account
for only a partial explanation of certainty. We show that both the overall
certainty and individual aspects can be predicted with pre-trained language
models, providing a more complete picture of the author's intended
communication. Downstream analyses on 431K scientific findings from news and
scientific abstracts demonstrate that modeling sentence-level and aspect-level
certainty is meaningful for areas like science communication. Both the model
and datasets used in this paper are released at
https://blablablab.si.umich.edu/projects/certainty/.
