You seem to be conflating quite a few axes of variation. What you're describing as "frequentist" is descriptive statistics using parametric models, which is only one possible way of using a frequentist interpretation of probability.
Nonparametric statistics is probably the biggest active area of frequentist-statistics research, in which case fitting parameters of models isn't exactly what you're doing (though there is some sort of model-building process, and some arguing about what constitutes a parameter).
In addition, predictive statistics is a quite large area of frequentist statistics, and it does precisely what you call "Bayesian" steps #4 and #5, except within a frequentist framework: you fit a predictive model, which may include uncertainty estimates in its predictions, and then feed that model's output through something decision-theoretic, like a risk function.
Bayesian decision theory and frequentist decision theory do look different, but it's not as if frequentists don't have a decision theory (and a real one, not just "use the model if it has a low enough p-value")...
Conflating - yes, hell-yes, I am simplifying as much as possible. I do use steps #4 and #5 within the predictive model including uncertainty estimates. My step frequentist 4 refers to this. The steps aren't completely aligned.
My comment is a description of the difference as it affects me, and not necessarily capturing all of the effects on others. Please do expand...
I suppose to me it's more of a difference of decision-theoretic approaches, which come up with decision rules that make "best" decisions given the data, under certain definitions of "best", versus descriptive-statistics approaches, which aim to summarize the data, test hypotheses, report significant correlations, etc. I can buy many of the arguments for decision-theoretic approaches (especially if you are in fact making decisions), but that doesn't necessarily tell me why I should use a specifically Bayesian decision-theoretic approach.
Nonparametric statistics is probably the biggest active area of frequentist-statistics research, in which case fitting parameters of models isn't exactly what you're doing (though there is some sort of model-building process, and some arguing about what constitutes a parameter).
In addition, predictive statistics is a quite large area of frequentist statistics, and it does precisely what you call "Bayesian" steps #4 and #5, except within a frequentist framework: you fit a predictive model, which may include uncertainty estimates in its predictions, and then feed that model's output through something decision-theoretic, like a risk function.
Bayesian decision theory and frequentist decision theory do look different, but it's not as if frequentists don't have a decision theory (and a real one, not just "use the model if it has a low enough p-value")...