Nullius in Verba Smriti Mehta and Daniël Lakens

In advance of the next three episodes discussing the Philosophical Psychology lectures by Paul E. Meehl, we present a brief reading from his autobiography in A history of psychology in autobiography.
Meehl, P. E. (1989). Paul E. Meehl. In G. Lindzey (Ed.), A history of psychology in autobiography (Vol. 8, pp. 337–389). Stanford, CA: Stanford University Press.

In this episode, we discuss objectivity and disinterestedness in science. We talk about norms, values, interests, and objectivity in research practice, peer review, and hiring decisions. Is it possible to be completely objective? Is objectivity a feature of epistemic products or epistemic processes? And most importantly, how would you objectively rate this podcast?
 
Shownotes
Armstrong, J. S. (1979). Advocacy and objectivity in science. Management Science, 25(5), 423–428.
Declaration of Interest by Stephen Senn: http://senns.uk/Declaration_Interest.htm
Djørup, S., & Kappel, K. (2013). The norm of disinterestedness in science; a restorative analysis. SATS, 14(2). https://doi.org/10.1515/sats-2013-0009
Elliott, K. C. (2017). A Tapestry of Values: An Introduction to Values in Science. Oxford University Press. https://doi.org/10.1093/acprof:oso/9780190260804.001.0001
Feyerabend, Paul. "How to defend society against science." Philosophy: Basic Readings (1975): 261-271.
Jamieson, K. H., McNutt, M., Kiermer, V., & Sever, R. (2019). Signaling the trustworthiness of science. Proceedings of the National Academy of Sciences, 116(39), 19231–19236. https://doi.org/10.1073/pnas.1913039116
Janack, M. (2002). Dilemmas of objectivity. Social Epistemology, 16(3), 267-281.
John, S. (2021). Objectivity in science. Cambridge University Press.
Merton, R. K. (1973). The sociology of science: Theoretical and empirical investigations. University of Chicago Press.
Mitroff, I. I. (1974). Norms and Counter-Norms in a Select Group of the Apollo Moon Scientists: A Case Study of the Ambivalence of Scientists. American Sociological Review, 39(4), 579–595. https://doi.org/10.2307/2094423
Mitroff, I. I. (1974). The subjective side of science: A philosophical inquiry into the psychology of the Apollo moon scientists (First Edition). Elsevier.
A Russian polar researcher has been charged trying to stab a colleague to death at a remote Antarctic base https://www.businessinsider.com/sergey-savitsky-alleged-attempted-murder-at-antarctic-bellingshausen-2018-10 
Stamenkovic, P. (2023). Facts and objectivity in science. Interdisciplinary Science Reviews, 1–22. https://doi.org/10.1080/03080188.2022.2150807
 

In this episode, we discuss the role of criticism in science. When is criticism constructive as opposed to obsessive? What are the features of fair and useful scientific criticism? And should we explicitly teach junior researchers to both give and accept criticism?
 
Shownotes:
Babbage, C. (1830). Reflections on the Decline of Science in England: And on Some of Its Causes.
Prasad, Vinay, and John PA Ioannidis. "Constructive and obsessive criticism in science." European journal of clinical investigation 52.11 (2022): e13839.
Lakatos, I. (1968, January). Criticism and the methodology of scientific research programmes. In Proceedings of the Aristotelian society (Vol. 69, pp. 149-186). Aristotelian Society, Wiley.
LOWI: https://lowi.nl/en/home/ As an independent advisory body it plays a role in the complaints procedure about alleged violations of principles of research integrity.
Holcombe, A. O. (2022). Ad hominem rhetoric in scientific psychology. British Journal of Psychology, 113(2), 434–454. https://doi.org/10.1111/bjop.12541
Daniel C. Dennett: I've Been Thinking https://wwnorton.com/books/9780393868050 
Phillip Stark textbook chapter on logical fallacies: https://www.stat.berkeley.edu/~stark/SticiGui/Text/reasoning.htm 
Gelman, A., & Tuerlinckx, F. (2000). Type S error rates for classical and Bayesian single and multiple comparison procedures. Computational Statistics, 15(3), 373–390. https://doi.org/10.1007/s001800000040
Popper, K. R. (1959). The logic of scientific discovery. Routledge.
PubPeer: https://pubpeer.com
 

In this episode, we continue discussing Dubin’s 8-step method for theory building. We discuss the measurement of theoretical constructs, using logical propositions to make falsifiable predictions from theories, and the importance of specifying boundary conditions. 
 
Shownotes
Jaccard, J., & Jacoby, J. (2010). Theory Construction and Model-building Skills: A Practical Guide for Social Scientists. Guilford Press.
McGuire, W. J. (1973). The yin and yang of progress in social psychology: Seven koan. Journal of Personality and Social Psychology, 26(3), 446–456. https://doi.org/10.1037/h0034345
Simons, D. J., Shoda, Y., & Lindsay, D. S. (2017). Constraints on Generality (COG): A Proposed Addition to All Empirical Papers. Perspectives on Psychological Science, 12(6), 1123–1128. https://doi.org/10.1177/1745691617708630
Norm Macdonald: The Professor of Logic
Raven Paradox: https://platonicrealms.com/encyclopedia/Hempels-Ravens-Paradox 
Pavlov, I. (1936). Bequest of Pavlov to the Academic Youth of His Country. Science, 83(2155), 369–370. https://doi.org/10.1126/science.83.2155.369
 

In this episode we discussed the 8-step method of theory building proposed by Robin Dubin in his classic 1969 book Theory Building.
 
Shownotes
Dubin, R. (1969). Theory building. Free Press. http://catalog.hathitrust.org/api/volumes/oclc/160506.html
Lynham, S. A. (2002). Quantitative Research and Theory Building: Dubin’s Method. Advances in Developing Human Resources, 4(3), 242–276. https://doi.org/10.1177/15222302004003003
Elms, A. C. (1975). The crisis of confidence in social psychology. American Psychologist, 30(10), 967.
Meehl, P. E. (1978). Theoretical Risks and Tabular Asterisks: Sir Karl, Sir Ronald, and the Slow Progress of Soft Psychology. Journal of Consulting and Clinical Psychology, 46(4), 806–834. https://doi.org/10.1037/0022-006X.46.4.806
Swedberg, R. (2014). The art of social theory. Princeton University Press.
Ben Wright: https://en.wikipedia.org/wiki/Benjamin_Drake_Wright
Yarkoni, T., & Westfall, J. (2017). Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning. Perspectives on Psychological Science, 12(6), 1100–1122. https://doi.org/10.1177/1745691617693393
Isaac, M. G., Koch, S., & Nefdt, R. (2022). Conceptual engineering: A road map to practice. Philosophy Compass, 17(10), e12879. https://doi.org/10.1111/phc3.12879
 

In this episode, we discuss the barriers to cumulative science, including inconsistent measurement tools, overreliance on single studies, and the large volume of research publications. Can replications, interdisciplinary collaborations, and prospective meta-analyses help us solve this issue? Can AI solve all our problems?  And do most scientists treat their theories like toothbrushes?
 
Shownotes
Opening quote by George Sarton
Sarton, G. (1927). Introduction to the History of Science (Vol. 376). 

Is Science Cumulative? a Physicist Viewpoint: https://link.springer.com/chapter/10.1007/978-1-4020-6279-7_10
Psychological Methods. (2009). Special Issue: Multi-Study Methods for Building a Cumulative Psychological Science.
Walter Mischel, Becoming a Cumulative Science 
Dorothy Bishop - Why we need cumulative science (AIMOS)
Watkins, J. W. (1984). Science and Skepticism. Princeton University Press.