> 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
