Episode 83: Codices de Moribus Scientificis - I

In today’s episode, we discuss the classic 1959 Rede Lecture by scientist and novelist C. P. Snow. We reflect on whether there is still a divide between the humanities and the sciences, how important it is to be educated in both, whether the humanities and the sciences care just as much about the truth, whether scientists need to know Shakespeare, and what the second law of thermodynamics is about. Enjoy!   Snow, Charles Percy (2001) [1959]. The Two Cultures. London: Cambridge University Press. ISBN 978-0-521-45730-9. Pickering (1995) The Mangle of Practice. https://archive.org/details/mangleofpractice0000pick/page/n5/mode/2up  Rutger Bregman: https://nl.wikipedia.org/wiki/Rutger_Bregman  

In this two-part episode, we examine scientific codes of conduct, using the Netherlands Code of Conduct for Research Integrity as our primary example. We discuss why such codes are necessary, where their guiding principles come from, and how (and whether) they are enforced. We also discuss many of the standards of good research practice derived from said principles. 

In this two-part episode, we examine scientific codes of conduct, using the Netherlands Code of Conduct for Research Integrity as our primary example. We discuss why such codes are necessary, where their guiding principles come from, and how (and whether) they are enforced.

In this episode, we will reflect on how governments should support science, from the 1945 policy report Science: The Endless Frontier by Vannevar Bush, and then move on to the recent policy document by the Trump Administration 'Science: A New Golden Age'. We reflect on what we like about the policy report and what we like less. Is the linear model from basic research to application indeed outdated? Are metascience units a good idea? And will AI accelerate and radically transform how we do science? Enjoy. Shownotes: Bush, Vannevar. (1945). Science The Endless Frontier. http://archive.org/details/in.ernet.dli.2015.212068 Bush, Vannevar. (1970). Pieces of the action. New York, Morrow. http://archive.org/details/piecesofaction00bush Science: A Golden Age https://www.whitehouse.gov/wp-content/uploads/2026/07/Science-A-New-Golden-Age.pdf     

In this episode, we will discuss multiverse analysis–what it is, why we use it, and how we should use it. We also discuss many analyst projects, and Daniel's new preprint on multiverse analysis.    Shownotes Auspurg, K. (2025). Robustness is better assessed with a few thoughtful models than with billions of regressions. Proceedings of the National Academy of Sciences, 122(43), e2521917122. Chamberlin, T. C. (1890). The method of multiple working hypotheses. Science, (366), 92–96. Dochtermann, N. A., & Jenkins, S. H. (2011). Developing multiple hypotheses in behavioral ecology. Behavioral Ecology and Sociobiology, 65(1), 37–45. Dreber, A., & Johannesson, M. (2025). The credibility gap: Evaluating and improving empirical research in the social sciences. Routledge. Gelman, A., & Loken, E. (2013). The garden of forking paths: Why multiple comparisons can be a problem, even when there is no “fishing expedition” or “p-hacking” and the research hypothesis was posited ahead of time. Department of Statistics, Columbia University, 348(1-17), 3. Knief, U., & Forstmeier, W. (2025). Are our 95% CIs only worth 45% confidence? bioRxiv, 2025-08. Lakens, D., Rasti, S., & Tunç, M. N. (2026). There is only one correct analysis. (Preprint) Platt, J. R. (1964). Strong Inference: Certain systematic methods of scientific thinking may produce much more rapid progress than others. Science, 146(3642), 347–353. Simonsohn, U., Simmons, J. P., & Nelson, L. D. (2020). Specification curve analysis. Nature Human Behaviour, 4(11), 1208–1214. Steegen, S., Tuerlinckx, F., Gelman, A., & Vanpaemel, W. (2016). Increasing transparency through a multiverse analysis. Perspectives on Psychological Science, 11(5), 702–712. Wimsatt, W. C. (2007). Re-engineering philosophy for limited beings: Piecewise approximations to reality. Harvard University Press.  

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)