Fake research produces results that are invalid from the start. I take such research to be characterised by three jointly sufficient features. It is severely methodologically defective, and the relevant defects support certain nonepistemic (social, political, economic) interests and objectives, while the relevant objectives typically concern the interference with attempts at political regulation. I deal with two kinds of claimed fake research. One is agnotological ploys in which scientific dissent is created by interested parties from industry or politics in order (...) to support their own partisan goals. Another one is the populist antiscience movement that suspects fake research in the scientific mainstream. I suggest three remedies to reduce or eliminate the impact of fake research: disclosing fallacies, improving the understanding of scientific methods, and distinguishing more clearly between science and politics in political decision-making. (shrink)

Some authors claim that minimal models have limited epistemic value (Fumagalli, 2016; Grüne-Yanoff, 2009a). Others defend the epistemic benefits of modelling by invoking the role of robustness analysis for hypothesis confirmation (see, e.g., Levins, 1966; Kuorikoski et al., 2010) but such arguments find much resistance (see, e.g., Odenbaugh & Alexandrova, 2011). In this paper, we offer a Bayesian rationalization and defence of the view that robustness analysis can play a confirmatory role, and thereby shed light on the potential of minimal (...) models for hypothesis confirmation. We illustrate our argument by reference to a case study from macroeconomics. At the same time, we also show that there are cases in which robustness analysis is detrimental to confirmation. We characterize these cases and link them to recent investigations on evidential variety (Landes, 2020b, 2021; Osimani and Landes, forthcoming). We conclude that robustness analysis over minimal models can confirm, but its confirmatory value depends on concrete circumstances. (shrink)