Two early thoughts as I am reading:
1. I agree with the description of empirical work as a "craft". When I teach, I always tell students that econometrics (in practice) is as much "art as it is science". And, I try to point and what points in an analysis we have left the science realm and have entered the art realm.
2. The "system" pillar and "vernacular knowledge" seem incontrovertible to me, but the "scale" part is less clear. Yes, we don't want to "sweat the small stuff". But, this requires knowledge that the "stuff" is "small". Too often researchers appeal to the "smallness" of a problem as a justification for ignoring it, but in fact have no idea if the problem is small or not. Thus, ignoring "small" stuff runs the very risk of inducing a lack of credibility. Two common examples of this are issues of measurement error and the choice between LPM/probit/other binary choice models.
1. I think where the book diverges from "art vs. science" is its emphasis on credibility. I think most (social) scientists wouldn't say that the artistic aspects of an analysis lend it credibility, and they might also think of the artistic choices as not being of much consequence. Taking up your binary outcome model example, it seems like folks tend to choose probit vs. logistic regression based on personal preference, but one is probably truly more appropriate for any given analysis. "Craftsmanship" points out that there are better and worse ways, more and less correct ways, of doing the "art" part of the analysis. 2. I view the "scale" chapter as demonstrating how to decide where the "system" ends and begins. There's always a tradeoff to modeling more stuff, but how can you know whether modeling something is worthwhile? Also, the chapter is about how you can know that something is small, rather than just assuming it away. I agree that folks often ignore "small" stuff that might actually be pretty important on the basis of convenience, but the books approach is the opposite: rather than centering your analysis around a model or theory and then assuming away anything inconvenient, you use scale rigorously up front to determine the boundaries of your system.