# Investigating Disagreement in the Scientific Literature

arXiv: https://arxiv.org/abs/2107.14641 · HTML: https://mlsanity.com/paper/2107.14641/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 92 references that are in the map.
- First posted on arXiv 30 Jul 2021.

## Builds on (most cited first, 4 of 4)

- [From Louvain to Leiden: guaranteeing well-connected communities](https://mlsanity.com/paper/1810.08473/) (2018)
- [Citation concept analysis (CCA) - A new form of citation analysis revealing the usefulness of concepts for other researchers illustrated by two exemplary case studies including classic books by Thomas S. Kuhn and Karl R. Popper](https://mlsanity.com/paper/1905.12410/) (2019)
- [A Scalable and Adaptive Method for Finding Semantically Equivalent Cue Words of Uncertainty](https://mlsanity.com/paper/1710.08327/) (2017)
- [Characterizing in-text citations in scientific articles: A large-scale analysis](https://mlsanity.com/paper/1710.03094/) (2017)

## Built on by (most cited first, 3 of 3)

- [Did AI get more negative recently?](https://mlsanity.com/paper/2202.13610/) (2022)
- [Insights Into the Nutritional Prevention of Macular Degeneration based on a Comparative Topic Modeling Approach](https://mlsanity.com/paper/2309.00312/) (2023)
- [Citing Less Critically: LLMs Reshape the Rhetoric and Reach of Scientific Citation](https://mlsanity.com/paper/2609.01432/) (2026)

## Abstract (arXiv)

Disagreement is essential to scientific progress. However, the extent of disagreement in science, its evolution over time, and the fields in which it happens, remains poorly understood. Leveraging a massive collection of English-language scientific texts, we develop a cue-phrase based approach to identify instances of disagreement citations across more than four million scientific articles. Using this method, we construct an indicator of disagreement across scientific fields over the 2000-2015 period. In contrast with black-box text classification methods, our framework is transparent and easily interpretable. We reveal a disciplinary spectrum of disagreement, with higher disagreement in the social sciences and lower disagreement in physics and mathematics. However, detailed disciplinary analysis demonstrates heterogeneity across sub-fields, revealing the importance of local disciplinary cultures and epistemic characteristics of disagreement. Paper-level analysis reveals notable episodes of disagreement in science, and illustrates how methodological artifacts can confound analyses of scientific texts. These findings contribute to a broader understanding of disagreement and establish a foundation for future research to understanding key processes underlying scientific progress.