Socio-environmental modeling shows physics-like confidence with water modeling surpassing it in numerical claims
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 modeling display a degree of linguistic assertiveness akin to physics. Water modeling 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 modeling socio-environmental processes, especially water modeling, are epistemically justified.
Authors
- Razi Sheikholeslami (ORCID: https://orcid.org/0000-0001-8004-1985)
- Abdolrasoul Sobhani (ORCID: https://orcid.org/0000-0002-7570-9810)
- Arnald Puy (ORCID: https://orcid.org/0000-0001-9469-2156)
- Lieke Anna Melsen (ORCID: https://orcid.org/0000-0003-0062-1301)
- E. M. Murray
- Andrea Saltelli (ORCID: https://orcid.org/0000-0003-4222-6975)
- M. Sadegh Khanjani
- Samuele Lo Piano (ORCID: https://orcid.org/0000-0002-2625-483X)
- Nanxin Wei
- Adrián Carmona (ORCID: https://orcid.org/0000-0002-6706-3458)
- Kai R. Larsen (ORCID: https://orcid.org/0000-0002-8812-9866)
- Alessio Lachi (ORCID: https://orcid.org/0000-0002-0764-0866)
- David Gefen (ORCID: https://orcid.org/0000-0002-0184-9167)
- Ethan Bacon
- Samuel Flinders
- Seth N Linga
Institutions
- University of Leeds (GB)
- Sharif University of Technology (IR)
- Universitat Pompeu Fabra (ES)
- University of Colorado Boulder (US)
- Saint Camillus International University of Health and Medical Sciences (IT)
- University of Bergen (NO)
- University of Reading (GB)
- Drexel University (US)
- University of Birmingham (GB)
- Wageningen University & Research (NL)
Publication Details
- Journal
- iScience
- Published
- 2025-03-12
- DOI
- https://doi.org/10.1016/j.isci.2025.112184
- Citations
- 3
- Primary Topic
- Environmental Monitoring and Data Management
- Type
- article
- Field-Weighted Citation Impact
- 7.04
Funders
- Government of the United Kingdom
- UK Research and Innovation
- HORIZON EUROPE Framework Programme