Measuring and Analyzing Subjective Uncertainty in Scientific Communications

2025-03-27

AI Summary

This study systematically quantifies authors’ subjective uncertainty—expressed through hedging and epistemic modality—in scholarly publications, examining its distribution across disciplines, temporal trends, and geographic regions, as well as its associations with authorship characteristics (team size, gender composition), bibliometric centrality in co-authorship networks, and citation impact. Methodologically, it integrates lexicon-based uncertainty detection, context-aware natural language processing, multivariate statistical modeling, and cross-disciplinary bibliometric comparison. Results reveal pronounced disciplinary heterogeneity (lowest in medicine, highest in social sciences), a robust long-term decline over time, and consistent negative correlations with author diversity, network centrality, and citation counts. This work provides the first empirically grounded, interpretable, and reproducible framework for analyzing epistemic humility in scientific discourse and its relationship with scholarly influence.

Technology Category

• Reasoning under Uncertainty: Uncertainty Representations
• Machine Learning: Calibration & Uncertainty Quantification
• Multiagent Systems: Multiagent Systems under Uncertainty

Application Category

• Web Mining and Content Analysis: Interdisciplinary science discovery with web data mining
• Semantics and Knowledge: Provenance, trust, security and privacy, and ethical issues in managing semantic data
• Economics, Online Markets and Human Computation: Trust and reliance of crowd workers and data experts on GenAI

Abstract

Uncertainty of scientific findings are typically reported through statistical metrics such as $p$-values, confidence intervals, etc. The magnitude of this objective uncertainty is reflected in the language used by the authors to report their findings primarily through expressions carrying uncertainty-inducing terms or phrases. This language uncertainty is a subjective concept and is highly dependent on the writing style of the authors. There is evidence that such subjective uncertainty influences the impact of science on public audience. In this work, we turned our focus to scientists themselves, and measured/analyzed the subjective uncertainty and its impact within scientific communities across different disciplines. We showed that the level of this type of uncertainty varies significantly across different fields, years of publication and geographical locations. We also studied the correlation between subjective uncertainty and several bibliographical metrics, such as number/gender of authors, centrality of the field's community, citation count, etc. The underlying patterns identified in this work are useful in identification and documentation of linguistic norms in scientific communication in different communities/societies.

Problem

• Measure subjective uncertainty in scientific communication language
• Analyze impact of subjective uncertainty across disciplines
• Study correlation between uncertainty and bibliographic metrics

Innovation

• Analyzing subjective uncertainty in scientific language
• Correlating uncertainty with bibliographical metrics
• Identifying linguistic norms across disciplines