All of which is why we should limit our attempts to do numerical analysis for
this topic, and worry far more about the basics,
including such things as interaction (in)sensitivities, group tone and style,
and observable misbehaviors, all of which are likely to produce biasing
results.
Certainly useful, but it is easy to inject one's own bias into such processes,
and to overlook other factors. I may be biased, but I have the impression that
the largest source of bias in IESG selection is the need to secure funding for
the job, which effectively self-select people working for large companies
making networking products. Gender may be the least of the problems there;
there are other dimensions of diversity, e.g. academic vs. industry, network
equipment versus internet service providers, software versus hardware, etc.
Only a fraction of these segments can afford to have someone working full-time
on the IESG. Now, having to work full time is a bit much for a volunteer
position, and we may want to consider ways to remedy that.
-- Christian Huitema