In a sense we do have this: engineering and finance. Engineering turns good hard science into new tools, machines and weapons, and Finance turns good (predictive) soft science into new ways to make money.
I think this is a common critique, but I also think it is missing the point. What if the question of interest isn't so easily verifiable like in Engineering? Do we just throw up our hands and give up on those questions? [The alternative to good social science is not no social science, it’s bad social science](https://statmodeling.stat.columbia.edu/2021/03/12/the-social...).
Finance is also a bit tautological in this regard. It seems that often prediction models are impossible to disprove (e.g., our arbitrage method doesn't work anymore, the market updated). Yes good for putting skin in the game, but doesn't seem like it does much to advance our long-term understanding of humans.
Some things may well be complex enough that it's simply impossible, with the amount of resources available to the average university, to conduct a thorough enough study on a representative enough sample that accounts for enough confounding factors to make a statistically sound prediction that generalises. If this were the case for a significant proportion of the subjects of study of a particular field, then it might well be better to "give up" and admit we don't and cannot know, otherwise we're essentially creating a factory for bad science (as the available resources relative to the scope of the problem aren't sufficient to create good science, and there's no negative feedback to stop the bad science).
We can bikeshed what makes something a "science" till the cows come home but the philosophy of science and epistemology were not settled with Bacon and Popper - the end goal has always understanding in the broadest sense. Those studies have value as long as they help someone make sense of and adapt to the social systems they're in. It does mean though that those studies should be approached with extreme caution (see the decades wasted on string theory) and anyone basing their research off past results needs to carefully validate their assumptions.
[1] I think in this case "predictive" as a scientific term of art is too restricting. Social sciences often deal with very personal interactions that appear nondeterministic at the scale of a society but are relatively predictable when applied to a stereotypical office or school setting.
At the very least, it seems to me like the person I originally responded to would also disagree with judging social sciences for its "utility" - the article they linked specifically contrasted it with the natural sciences that "solve problems".
Closest analogy off the top of my head is psychiatric drugs: their efficacy is generally bottom of the barrel except for some group with factor X (each drug has their own unique factor X). For the vast majority of these drugs, we have no method of screening for whether a person has factor X - we don't even know what it is most of the time - so doctors have to go through a process of trial and error with patients until they find the right drug or combination. Once they do, it's like a night and day difference for the patient, yet if we applied the same standard of evidence for psychiatric drugs that we do for blood pressure pills, we'd never make any progress. A lot of the drugs look like they don't work in phase III and we have no way to predict which drug which help which patient but the patients figure it out with their doctors because they have actionable data, even if it isn't predictive in general.
In the long run it usually comes out, but the run can be longer than you think, and you may not be where you think in it with regard to any particular current theory. I wonder what things we know all "know" are proven by science will be dismissed by later generations. (I personally guess a lot of genetics-related stuff will be).
(Note that something doesn't need to be verifiable, reliable, or true to be "actionable". You can act on anything...)