Daarjuist een rebutal tegengekomen die al een heel stuk beter wetenschappelijk onderbouwt is en aangeeft waarom Damore veel te wilde conclusies trekt uit zijn assumpties gebaseerd op grotendeels irrelevante data.
James Damore cites evidence of minor differences in personality between men and women, which is true, such evidence exists.
There is no evidence those differences mean what he says they mean. There is no evidence those differences would cause the gender disparity. The differences in personality traits are very small and that's far, far smaller than the gender disparity. There is, in fact, no evidence that the differences would not work to make women more common than men in IT barring other factors like culture/society/etc. Furthermore
personality is at best 50% genetic, so the small differences could be entirely cultural in origin.
So not only does he assume there's a causal link here, but he assumes a direction, and that the small differences (on average) somehow magnify into massive gender disparity. With no evidence to back any of this up.
Based on the slight differences between men and women in personality and zero evidence this affects the job, he wants to remove all programs to get more women involved in IT AND all programs to get minorities involved (he does not even bother trying to justify this).
So that's both sexist and racist.
There is lots of evidence, which he does not cite, that
women are just as capable as men with math and science. And there's
lots
and
lots
and
lots
and
lots
and
lots
and
lots1
and
lots -- has
this link to study on implicit bias, so read it if you don't understand it.
and
lots
of evidence that women and minorities are discriminated against throughout their lives and that this influences what they decide to do with their lives. (Note: Citations are focused on STEM, but it applies to far more than that, of course).
He says that implicit bias training doesn't work, but
research indicates that it does though more longitudinal studies on programs are needed Indeed while
there are some methods of improving diversity that don't work,
it doesn't seem like Google uses any of them, and in fact they seem to use the successful methods (though I have not found anything indicating they use accountability, so there's room for improvement).