Measuring Sentence-Level and Aspect-Level (Un)certainty in Science Communications

Abstract

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 sentencelevel 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/

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 22 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.