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Measuring and Analyzing Subjective Uncertainty in Scientific Communications

arXiv 2503.21114 ↗First posted 27 Mar 2025cs.DL cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 41 references that are in the map. First posted on arXiv 27 Mar 2025.

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References · 10

  1. 2019BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehensioncited by 5,893
  2. 2019Benchmarking Zero-shot Text Classification: Datasets, Evaluation and Entailment Approachcited by 308
  3. 2021Measuring Sentence-Level and Aspect-Level (Un)certainty in Science Communicationscited by 27
  4. 2021The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processingcited by 13
  5. 2021What's in a Name? -- Gender Classification of Names with Character Based Machine Learning Modelscited by 12
  6. 2017The Wisdom of Polarized Crowdscited by 9
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  8. 2024Enhancing Uncertainty Quantification in Drug Discovery with Censored Regression Labelscited by 5
  9. 2021A simple method for estimating the Lorenz curvecited by 2
  10. 2025Annotating Scientific Uncertainty: A comprehensive model using linguistic patterns and comparison with existing approachescited by 2

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