Journal article
Socio-environmental modelling shows physics-like confidence with water modelling surpassing it in numerical claims
iScience, v 28(4), 112184
Mar 2025
Featured in Collection : UN Sustainable Development Goals @ Drexel
Abstract
Several modern scientific fields rely on computationally-intensive mathematical models to study uncertain, complex socio-environmental phenomena such as the spread of a virus, climate change or the water cycle. However, the degree of epistemic commitment of these fields is unclear. By using machine learning to extract the knowledge claims of around 755,000 abstracts from 14 scientific fields spanning the human and physical sciences, we show that epidemic, integrated assessment and water modelling display a degree of linguistic assertiveness akin to physics. Water modelling surpasses even the most accurate physical sciences in substantiating knowledge claims with numbers, which are largely produced without accompanying uncertainty and sensitivity analysis. By exploring the balance between doubt and certainty in academic writing, our study reflects on whether the strong conviction and quantification of fields modelling socio-environmental processes, especially water modelling, is epistemically justified.
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Details
- Title
- Socio-environmental modelling shows physics-like confidence with water modelling surpassing it in numerical claims
- Creators
- Arnald Puy - University of BirminghamEthan Bacon - University of BirminghamAlba Carmona - University of BirminghamSamuel Flinders - University of BirminghamDavid Gefen - Drexel UniversityMohammad Khanjani - Sharif University of TechnologyKai R. Larsen - University of Colorado BoulderAlessio Lachi - Saint Camillus International University of Health and Medical SciencesSeth N. Linga - University of BirminghamSamuele Lo Piano - University of ReadingLieke A. Melsen - Hydrology and Environmental Hydraulics Group, Wageningen University, P.O. Box 9101, 6700 HB, The NetherlandsEmily Murray - University of BirminghamRazi Sheikholeslami - Sharif University of TechnologyAriana Sobhani - University of BirminghamNanxin Wei - University of BirminghamAndrea Saltelli - Pompeu Fabra University
- Publication Details
- iScience, v 28(4), 112184
- Publisher
- Elsevier
- Number of pages
- 15
- Grant note
- UK Research and Innovation under the UK government: EP/Y02463X/1
We thank Federico Ferretti for providing us with the astrophysics dataset. This work was funded by UK Research and Innovation under the UK government's Horizon Europe funding guarantee (project DAWN, PI A.P., EP/Y02463X/1) .
- Resource Type
- Journal article
- Language
- English
- Academic Unit
- Decision Sciences (and Management Information Systems)
- Web of Science ID
- WOS:001457385300001
- Scopus ID
- 2-s2.0-105000650529
- Other Identifier
- 991022041956604721
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- Collaboration types
- Domestic collaboration
- International collaboration
- Web of Science research areas
- Environmental Sciences