For me, the difference between ML and most of what we use stats for in classical research settings (Bayesian or otherwise) has less to do with the specific meth

For me, the difference between ML and most of what we use stats for in classical research settings (Bayesian or otherwise) has less to do with the specific methodology chosen, and more to do with the purpose of the exercise. With ML, the utility of the model is largely a function of its predictive power. You want to accomplish some task. "Make it go" With much of statistics in a research context (such as the social sciences as called out in the link), the interest is more on explanatory power of the independent variables. Most social scientists would happily trade a bit of predictive power for a more explanatory model that neatly maps back to a set of hypotheses. "Why does it go?"