Thanks to all who sent me links to the recent PNAS article by Ceci & Williams on "Understanding current causes of women's underrepresentation in science". I had read reports of the results, and now have read the article itself.
The authors of the article focused on some of the most commonly cited reasons for the underrepresentation of women in "math-intensive fields of science": discrimination in reviewing of proposals and manuscripts; being selected for interviews in faculty positions; and in hiring. They propose that current data do not support these as primary causes for the underrepresentation of women today.
Their main conclusion is that "differential gendered outcomes in the real world result from differences in resources attributable to choices, whether free or constrained", and that underrepresentation would be best alleviated through changes that take into account "differing biological realities of the sexes."
I think they make some important points with their study, and I believe that the current situation for women being evaluated for jobs, grants, and publications is better than it has ever been. However, I continue to see and hear examples of discrimination in reviews and hiring committees -- faculty who doubt that women have their own ideas (but have no trouble believing this about male candidates), or who don't like "aggressive" women (but think this is a fine trait in a man), are alive and well. These issues are not as obvious and widespread as in the past, but neither are they isolated, rare incidents that can safely be ignored as irrelevant to current practices.
I was surprised that the study focused so much on databases related to the life sciences, a realm of science in which, as the authors note, women now make up the majority of PhD recipients. I realize that some biological sciences are quite "math-intensive", but the authors seem to use this term to refer specifically to the non-biological sciences. It would therefore make sense to base conclusions primarily on studies other than those involving the life sciences.
(A quibble: In the sentence about how the number of PhDs awarded to women in the life sciences has increased, 13% is described as "orders of magnitude less" than 52%. It is not.)
In the section on Discrimination Against Women in Journal Reviewing, the authors rely in part on a study of acceptance rates for the journals Behavioral Ecology, the Journal of Biogeography, and Nature Neuroscience. Those all seem kind of life sciencey to me.
Similarly, in the section on Discrimination Against Women in Grant Funding, the authors rely on studies of databases of Medical Research Councils and similar organizations of various countries, including the NIH. There is also mention of NSF and the Australian Research Council, both of which cover a wide range of fields in science, engineering, education, and beyond. If possible, it would have been interesting to see a separate analysis of recent data for the "math-intensive" sciences.
Having seen such data for my own field, I believe that NSF works -- with some success -- to provide a "gender-fair grant review process", but I don't think the authors of this particular study have demonstrated that with their chosen databases. [I chose the phrase "works to provide" rather than a simple "provides" based on experiences such as this (which referred to, but did not specify, that the problem was offensive sexist comments by a program officer) and this and this and this, all of which the excellent and enlightened staff at NSF can and do filter so that there are no deleterious effects on female PIs].
The authors note that there are "more women in teaching-intensive, part-time posts where research resources are scarce", and attribute this to life-style choices or constraints. When discussing the situation at major research universities, the authors cite an NRC task force report that concluded that women were not at a disadvantage for interviews and offers (and may have a slight advantage) in a study of 6 fields of "natural science". That's great, but I think it is premature to propose, based on these data, that universities should discontinue efforts to train hiring committees to avoid bias (explicit or implicit).
Although ultimately not as convincing as it could be (owing to the datasets used), this is a useful study in that the authors try to focus our collective attention on additional factors that affect the underrepresentation of women in math-intensive fields of science, and suggest that universities explore new options for career tracks ("The linear career path of the modal [sic?] male scientist of the past may not be the only route to success.."). I agree; just don't throw out the methods that seem to be working so far. That would make the situation orders of magnitude worse than it already is for women in math-intensive fields of science.