This is a follow up to my previous look at whether the Hugos are more clique ridden by trying to use data on nominees with multiple nominations. Does that make sense? If not read the last episode or take a deep breath as it gets worse from here. Note these posts are all ‘thinking out loud’ – comments and critiques are more than welcome. Some useful comments from Yellowcake and Influxus from the last post on this topic will feed into the next follow up.
A counter argument I was considering was that while the multiple nomination issue didn’t seem to be getting worse according to my analysis, PERHAPS I was ignoring a key issue. Sure some people (e.g. Heinlein) got a lot of nominations but this was over a long career. The problem is people like [insert hate figure] who is a relative new comer but has already racked up X nominations.
Hmm. How to test that? Well here is my plan. Get the same data I used before – number of nominations, average (mean) year of nomination but now add the standard deviation of the year of nomination! Nominees with long careers will have
a bigger standard deviation. Nominees with shorter careers will be nominated over a shorter time period and hence a small standard deviatio
n. Standard deviation in this case is also measured in years.
But…lots of people have only 1 or 2 nominations. Consequently the data will be swam
ped by the people with few nominations. Solution! Filter out the people with less than 3 nominations. Now I’m accounting for all three aspects, time period, nomination period, 3 or more nominations.