That’s a very pessimistic view of what science can do. It’s true that the softer fields are harder to experiment in but scientists have many tools for that – it’s not like people just gave up the first time they hit a problem harder to test than Mendel’s peas.
As a few examples, you could do broad reviews to see if different cultures have different outcomes (e.g. the former communist bloc countries having higher percentages of female engineers and scientists undercuts the arguments that this is dictated by biology), draw comparisons from other fields (e.g. the same justifications were used to excuse gender ratios in law, medicine, music, etc. but cultural changes brought greater equality on a timescale far far than biology could change), or try to find underlying biological explanations — e.g. we didn’t need a cultural change to explain why women aren’t power lifting as much on average because there’s an underlying mechanism and there are individuals who have outlier levels of testosterone & other factors and those people generally perform as the biological mechanism would predict.
Most importantly, this could start by questioning the assumptions used to explain the status quo. For example, did the requisite skills change after the early 1980s when female CS participation declined? Lots of men like to excuse different participation rates with some argument based on mathematical or spatial skills, which could be tested to see how many successful people rely on those skills vs. more equal fields like math or chemistry.
As a few examples, you could do broad reviews to see if different cultures have different outcomes (e.g. the former communist bloc countries having higher percentages of female engineers and scientists undercuts the arguments that this is dictated by biology), draw comparisons from other fields (e.g. the same justifications were used to excuse gender ratios in law, medicine, music, etc. but cultural changes brought greater equality on a timescale far far than biology could change), or try to find underlying biological explanations — e.g. we didn’t need a cultural change to explain why women aren’t power lifting as much on average because there’s an underlying mechanism and there are individuals who have outlier levels of testosterone & other factors and those people generally perform as the biological mechanism would predict.
Most importantly, this could start by questioning the assumptions used to explain the status quo. For example, did the requisite skills change after the early 1980s when female CS participation declined? Lots of men like to excuse different participation rates with some argument based on mathematical or spatial skills, which could be tested to see how many successful people rely on those skills vs. more equal fields like math or chemistry.