> 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