Abstract
Ten years into the replication crisis, many scientists are experiencing a deep sense of worry and scepticism. In reaction to this problem, an optimistic wave of researchers has taken the lead, turning their scientific eyes onto science itself, with the aim of making science better. These metascientists have made progress studying causes of the crisis and proposing solutions. They have identified questionable research practices and bad statistics as potential culprits (Simmons et al., 2011, John et al., 2012). They have defended statistical (Cumming, 2012; Lee and Wagenmakers, 2013) and publication reforms (Chambers, 2013; Vazire, 2015) as solutions. Lastly, they are designing technological tools (benefiting from developments in related fields such as data science, machine learning, and complexity science) to support such reforms. The term metascience precedes the replication crisis. However, only now metascience is becoming institutionalised: there is an increasing community of practitioners, societies, conferences, and research centres. This institutionalisation and its perils require philosophical attention. It is worth stepping back and asking foundational questions about it. How did metascience emerge? Where does the novelty of metascience lie? How does metascience relate to other fields that take science as their subject matter? This talk focuses on the conceptual origins of metascience. I explore three different models of discipline creation and change, and seek to understand whether they can make sense of the emergence of metascience. (1) First, on the sociological model, the emergence of metascience does not obey merely epistemic needs, and can also be explained as a fashion (e.g., Crane, 1969). (2) By contrast, on the Kunhian model (1970), metascience can be viewed as a scientific revolution (a term that metascientists sometimes use) that is necessary to move beyond a period of crisis. (3) Finally, on the spin-off model, similarly to how physics branched out from natural philosophy, metascience could become the natural successor of disciplines such as history and philosophy of science. After examining these models, I suggest that we should challenge the increasingly popular perception of metascience as a fully authoritative field, in particular, when it comes to understanding the causes of the replication crisis and finding its solutions. References Chambers, C. D. (2013). Registered Reports: A new publishing initiative at Cortex. Cortex, 49, 609–610. Crane, D. (1969). Fashion in Science: Does It Exist? Social Problems, 16(4), 433–441. Cumming, G. (2012). Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-analysis. Multivariate applications book series. Routledge. John, L. K., Loewenstein, G., & Prelec, D. (2012). Measuring the prevalence of questionable research practices with incentives for truth telling. Psychological Science, 23, 524–532 Kuhn, Thomas S (1970). The Structure of Scientific Revolutions. Chicago: University of Chicago Press. Lee, M. D. & Wagenmakers, E-J. (2013). Bayesian Cognitive Modeling: A Practical Course. Cambridge: Cambridge University Press. Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011). False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychological Science, 22, 1359–1366. doi:10.1177/0956797611417632 Vazire, S. (2015). Editorial. Social Psychological & Personality Science,7, 3–7.
