I think I disagree with this. To take a look at the first few points:
* I don't follow why the distribution of women in different fields "Does not look discrimination". Seems to me it could be adequately explained by either M, C, or a combination of both, theories
* I find the correlation between % of women in STEM/Theory and Gender Equality Index unconvincing for a few reasons. One is that there are regional + cultural correlations between countries that, unaccounted for, undermines the regression somewhat. The RHS plot also looks like it's influenced a lot by a few high leverage points on the right hand side.
* I have no idea how you get to "it’s merit, not sexism." from "M more cited than F, equally by M and F". To think that sexism operates only through the action of misogynist men is a naive and unhelpful view of the world. There are plenty of alternative, structural reasons that could cause a trend like this. To take one example, if women leave the field in greater numbers or earlier in their career than men, they could not present their findings at conferences or promote their papers in ways that would boost citations.
I know it's hard to fully engage with a talk when you just have the slides, but I think even with full generosity of interpretation, the points raised here are still lazy, weak points.
Furthermore, data is a tool. A very useful tool, but one among many. When we reach for data alone, while giving no weight at all to the lived experiences of our female friends and colleagues, we are throwing away real, useful information. This talk fails to really acknowledge any points beyond the bibliometric data. If you're really interested in getting to the truth, with no agenda, using only one limited source of information is a really poor way to do it.