Work

A large-scale multi-disciplinary analysis of uncertainty in research articles

Year: 2026

Type: article

Abstract: Scientific uncertainty is inherent to the research process and to the production of new knowledge. In this paper, we present a large-scale analysis of how scientific uncertainty is expressed in research articles. To perform this study, we analyze the Const-L dataset, which consists in 31,849 research articles across 16 disciplines published over more than two decades. To identify and categorize uncertainty expressions, we employ the UnScientify annotation system, a linguistically informed, rule-based approach. We examine the distribution of uncertainty across disciplines, over time, and within the structure of articles, and we analyze its contexts and objects. The results show that the Social Sciences and Humanities (SSH) tend to have a higher frequency of uncertainty expressions than other fields. Overall, uncertainty tends to decrease over time, though this trend varies across disciplines. Moreover, correlations can be observed between the uncertainty expressions and both article structure and length. Finally, our findings provide new insights into scientific communication, by indicating distinctive disciplinary patterns in the ways uncertainty is expressed, as well as shared and field-specific research objects associated with uncertainty.

Source: Data & Knowledge Engineering

DOI: 10.1016/j.datak.2026.102561

Authors: Nicolas Gutehrlé, Panggih Kusuma Ningrum, Iana Atanassova

Institutions: Centre de Recherches Interdisciplinaires et Transculturelles, Université Marie et Louis Pasteur, Institut Universitaire de France

Language: English

Topic: Discourse Analysis in Language Studies

Subfield: Literature and Literary Theory

Field: Arts and Humanities

Domain: Social Sciences

Open Access status: hybrid

Estimated APC paid: $3,000

Funder: Agence Nationale de la Recherche