Hedging and epistemic modality in academic discourse: linguistic markers of caution and non-categorical claiming

Abstract

The article examines how a compact set of epistemic markers – might, could, suggest, appear to – functions as a linguistic toolkit of hedging in English academic discourse, with a particular focus on research articles authored by Ukrainian scholars in wartime conditions. The study is grounded in a small, purpose-built corpus of English-language publications three disciplinary clusters – Applied Linguistics and Education, Biology and Life Sciences, Electrical Engineering – situated along a soft-hard knowledge continuum. All articles were examined using the Sketch Engine platform, and the analysis relies on open-access English corpora – primarily the English Web 2021 (enTenTen21) – to identify and quantify epistemic uses of the target markers, which were then analysed in terms of frequency, distribution across rhetorical sections, and functional load in the transition empirical observation to interpretive claiming. The findings show that the four markers are not interchangeable, but occupy distinct positions on a scale of epistemic commitment. Suggest is preferentially used authors infer relatively robust relationships their data, especially in Results and Discussion sections. Might and could cluster around hypothetical explanations, projections and scenario building, marking claims as plausible yet explicitly revisable. Appear to signals heightened caution, typically in multi-factorial or model-dependent contexts key variables cannot be fully observed. The density and patterning of these markers vary by discipline, with the highest concentration in interpretive, human-facing fields and lower, but strategically important, use in engineering texts at the interface between simulation and real-world recommendations. The article argues that careful calibration of epistemic modality is a central mechanism of responsible scientific communication, particularly under the russian-Ukrainian war of aggression, when data are fragmented and yet urgently needed for decision-making. The results have direct implications for teaching academic English and academic integrity in Ukrainian universities and outline directions for further corpus-based, contrastive and computational research on hedging in crisis-affected scholarly communities.