Nullius in Verba

After a short break due to busy calendars, we are back to discuss the role automation and artificial intelligence can play in science. Is AI evil, or does it just amplify the problems that were already present in science? Can automated AI driven checks help scientists to improve the way they work? If so, where are these tools helpful, and when? Enjoy. 

The Babbage quote is from Babbage in November 1839, recalling events in 1821; quoted in Harry Wilmot Buxton and Anthony Hyman (1988), Memoir of the Life and Labours of the Late Charles Babbage.

 

Alfred Whitehead: An Introduction to Mathematics (1911)

 

Blog about AI references: Evaluating Dr. Cuddy’s Claim that the Debunking of Power Posing is a Myth. https://daniellakens.blogspot.com/2026/05/evaluating-dr-cuddys-claim-that.html

 

Metacheck: DeBruine L, Mesquida C, Werner J, Lakens D (2026). metacheck: Check Research Outputs for Best Practices. doi:10.5281/zenodo.20704754, R package version 0.1.0, https://scienceverse.org/metacheck

 

Cummins, J., Clarke, B., Hussey, I., & Elson, M. (2026). RegCheck: A tool for structured comparisons between study registrations and papers (arXiv:2601.13330). arXiv. https://doi.org/10.48550/arXiv.2601.13330

In this episode, we continue our discussion of disagreement in science, shifting the conversation from why it matters to how to do it well.

 

Shownotes

Paul Graham. (2008). How to disagree. 

Rapoport's Rules. 

Cass Sunstien. The Rapoport Rules.

Preregistration is redundant, at best. 

An Evidence-Based Critique of the Cass Review 

Fiedler, K., Messner, C., & Bluemke, M. (2006). Unresolved problems with the “I”, the “A”, and the “T”: A logical and psychometric critique of the Implicit Association Test (IAT). European Review of Social Psychology, 17(1), 74–147. https://doi.org/10.1080/10463280600681248

Neyman, J. (1961). Silver Jubilee of My Dispute With Fisher. Journal of the Operations Research Society of Japan, 3, 145–154.

This is a two-part episode on the role of disagreement in science. In the first part, we discuss the "why," before moving on to the "how" in the next episode. Enjoy. 

 

Shownotes

Dellsén, F., & Baghramian, M. (2021). Disagreement in science: Introduction to the special issue. Synthese, 198(Suppl 25), 6011-6021.

Oreskes, N., & Conway, E. M. (2011). Merchants of doubt: How a handful of scientists obscured the truth on issues from tobacco smoke to global warming. Bloomsbury Publishing USA.

Popper, K. (1959). The Logic of Scientific Discovery. London: Hutchinson.

Seidel, M. (2021). Kuhn’s two accounts of rational disagreement in science: an interpretation and critique. Synthese, 198(25), 6023-6051.

Shaw, J. (2021). Feyerabend and manufactured disagreement: reflections on expertise, consensus, and science policy. Synthese, 198(25), 6053-6084.

In this episode, we discuss the problem of miscitation. How often are citations to the scientific literature outright misleading? Do we really need to spell out that people  are supposed to read what they cite? What can we learn from other fields? Or should we just live with the fact that a decent percentage of citations in the literature are wrong? Enjoy. 

 

Careless citations don't just spread scientific myths – they can make them stronger (Nature)

Cobb, C. L., Crumly, B., Montero-Zamora, P., Schwartz, S. J., & Martínez Jr, C. R. (2024). The problem of miscitation in psychological science: Righting the ship. American Psychologist, 79(2), 299–311.

Simmering, M. J., Fuller, C. M., Leonard, S. R., & Simmering, V. R. (2025). Cognitive biases and research miscitations. Applied Psychology, 74(1), e12589.

Qinyue Liu, Amira Barhoumi, Cyril Labbé. (2024). Miscitations in scientific papers: Dataset and detection. International Workshop on Bibliometric-enhanced Information Retrieval. Glasgow, United Kingdom.

Lazonder, A. W., & Janssen, N. (2022). Quotation accuracy in educational research articles. Educational Research Review, 35(1), https://doi.org/10.1016/j.edurev.2021.100430

James, W. (1914). The energies of men. New York : Moffat, Yard and Company. http://archive.org/details/energiesofmen00jameuoft

Beyerstein, B.L. (1999) Whence cometh the myth that we only use ten percent of our brains? In, S. Della Sala (Ed.), Mind Myths: Exploring Everyday Mysteries

Jergas, H., & Baethge, C. (2015). Quotation accuracy in medical journal articles—A systematic review and meta-analysis. PeerJ, 3, e1364. https://doi.org/10.7717/peerj.1364

Bruton, S. V., Macchione, A. L., Brown, M., & Hosseini, M. (2025). Citation Ethics: An Exploratory Survey of Norms and Behaviors. Journal of Academic Ethics, 23(2), 329–346. https://doi.org/10.1007/s10805-024-09539-2

Simkin, M., & Roychowdhury, V. (2006). Do You Sincerely Want to Be Cited? Or: Read Before You Cite. Significance, 3(4), 179–181. https://doi.org/10.1111/j.1740-9713.2006.00202.x

Simmering, M. J., Fuller, C. M., Leonard, S. R., & Simmering, V. R. (2025). Cognitive biases and research miscitations. Applied Psychology, 74(1), e12589. https://doi.org/10.1111/apps.12589

Bluebook: https://www.legalbluebook.com

In this two-part episode, we discuss incentives in science and academia. We discuss the various incentives in science, including recognition, citations, money, and the kick in the discovery.

 

Shownotes

Cole, S., & Cole, J. R. (1967). Scientific output and recognition: a study in the operation of the reward system in science. American Sociological Review, 377–390.

Crane, D. (1965). Scientists at major and minor universities: A study of productivity and recognition. American Sociological Review, 699–714.

Merton, R. K. (1963). Resistance to the systematic study of multiple discoveries in science. European Journal of Sociology/Archives Européennes de Sociologie, 4(2), 237–282.

Stephan, P. (2015). How economics shapes science. Harvard University Press.

Tal Yarkoni - No, it’s not The Incentives—it’s you

Tom Leher - Lobachevsky (1953)