P.S. I think Royall’s book is a classic worth reading by any serious student of statistical foundations, as a serious attempt to place the likelihoodist viewpoint above all. Nonetheless I regard all viewpoints as seriously incomplete for research in general. Consequently, assigning primacy to any one formal viewpoint risks a dangerous mind trap, inducing cognitive biases that stem from missing what that viewpoint is blind to and overweighting what it emphasizes. That caution applies to frequentist, Bayesian, likelihood and any other statistical system if treated as a philosophy instead of as a toolkit.
P.P.S. Chernoff’s quote appears much closer to my position than Royall’s. But in light of my perspectivist/toolkit view I would not label myself as anything so specific as a Bayesian decision theorist - although I think the theory is another one every serious student should study (and was a bold label to take in his generation - he was born 1923 and still alive!). Of course I also think serious study of both Fisherian and Neyman-Pearson-Wald frequentist theory is essential, especially for deconfounding the two and relating them to their Bayesian analogs (reference Bayes for Fisherian, Bayesian decision theory for NPW).